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An SE(3)-invariant autoencoder for generating the periodic structure of materials [ICLR 2022]

License: MIT License

Python 100.00%

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usccolumbia jihochoi jxzhangjhu zilong-yuan thecatofhs lcn1997 pincher-chen vladimir-laz adeeshkolluru suthzx xiangxiang920920 danielzuegner itzme-jp cnatthaphon sxie22 bursoft zuhalcakir richardxie1119 filipekstrm pmlyngby mary-try tum-daml michael-jimenez13 rcbraga takshan sparks-baird knc6 xinyu1905 lsdras zhongpc orbital-materials deneutoy chaoqu12 760-project cheng-jw muhamit slives-lab raolixiang-up dg981001 pandabear-67 aseemgill seongsukim-ml ai4chems jarvis-materials-design deepmolgen ethanwtl ciqinwang7 wangjunxiao mihirjagtap andreicnica izumitkh jianzhou711 dongfangyu-bruce ixsluo xqh19970407 bigd4 haidi-ustc yingli-claire andaero ykq98 huaizz-shawen jisujung928 feiji110 liuzhengx hexagonrose globle-thunder hezhengda espinalmaha cw-tan erikumble melvynfourier danielathk xinjianouyang dragonyanglong janghoonock-tri kdmsit colliner

cdvae's Issues

A question about the cdvae

Excuse me, I'm a first-year graduate student. My programming ability is limited. So I have a question.

I want to know what the MODEL_PATH is. I tried many paths, but none of them worked. this is the hint:

hydra.errors.MissingConfigException: Primary config directory not found.
Check that the config directory '\Users\12032\Desktop\cdvae-main\cdvae\run.py' exists and readable

OSS Error: The paging file is too small for this operation to complete.

when i run in command line :python cdvae/run.py data=carbon expname=carbon, above issue appear. i change batchsize to 1 and expand virtual memory in my computer, but this issue still remain. Does anyone know how can i fix this?

Why there is no checkpoint saved after train

Never mind, I have solved this problem.

How to extract structures and relevant properties after recon, gen and opt tasks?

Could you please explain how to extract data from the outputs of recon, gen and opt tasks? @txie-93
How will we control the number of generated structures? example: If someone wants to generate 100 structures

Problem with the reconstruction task

Hello, this software looks great!

However, I am having problems when I try to run the reconstruction task, I get the error below. I traced the error and did some printouts and the error seems to occur when the program tries to process the attribute (attr) num_atoms with value tensor([5] , device='cuda:0')

Any clues on what can be the problem would be very appreciated.

Best regards,
Luis

Traceback (most recent call last):
File "scripts/evaluate.py", line 280, in
main(args)
File "scripts/evaluate.py", line 194, in main
all_frac_coords_stack, all_atom_types_stack, input_data_batch) = reconstructon(
File "scripts/evaluate.py", line 69, in reconstructon
input_data_list = input_data_list + batch.to_data_list()
File "/home/chemist/.conda/envs/cdvae/lib/python3.8/site-packages/torch_geometric/data/batch.py", line 157, in to_data_list
return [self.get(i) for i in range(self.num_graphs)]
File "/home/chemist/.conda/envs/cdvae/lib/python3.8/site-packages/torch_geometric/data/batch.py", line 157, in
return [self.get(i) for i in range(self.num_graphs)]
File "/home/chemist/.conda/envs/cdvae/lib/python3.8/site-packages/torch_geometric/data/batch.py", line 90, in get
data = separate(
File "/home/chemist/.conda/envs/cdvae/lib/python3.8/site-packages/torch_geometric/data/separate.py", line 57, in separate
data_store[attr] = _separate(attr, batch_store[attr], idx, slices,
File "/home/chemist/.conda/envs/cdvae/lib/python3.8/site-packages/torch_geometric/data/separate.py", line 116, in _separate
if decrement and (incs.dim() > 1 or int(incs[idx]) != 0):
AttributeError: 'NoneType' object has no attribute 'dim'

expected scalar type Float but found Doubl

Got the following error. Anyone has any ideas what could be wrong?

Error executing job with overrides: ['data=carbon', 'expname=carbon', 'model.predict_property=True']
Traceback (most recent call last):
File "cdvae/run.py", line 166, in main
run(cfg)
File "cdvae/run.py", line 154, in run
trainer.fit(model=model, datamodule=datamodule)
File "/Users/wiltonkortkamp/opt/anaconda3/envs/cdvae/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 460, in fit
self._run(model)
File "/Users/wiltonkortkamp/opt/anaconda3/envs/cdvae/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 758, in _run
self.dispatch()
File "/Users/wiltonkortkamp/opt/anaconda3/envs/cdvae/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 799, in dispatch
self.accelerator.start_training(self)
File "/Users/wiltonkortkamp/opt/anaconda3/envs/cdvae/lib/python3.8/site-packages/pytorch_lightning/accelerators/accelerator.py", line 96, in start_training
self.training_type_plugin.start_training(trainer)
File "/Users/wiltonkortkamp/opt/anaconda3/envs/cdvae/lib/python3.8/site-packages/pytorch_lightning/plugins/training_type/training_type_plugin.py", line 144, in start_training
self._results = trainer.run_stage()
File "/Users/wiltonkortkamp/opt/anaconda3/envs/cdvae/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 809, in run_stage
return self.run_train()
File "/Users/wiltonkortkamp/opt/anaconda3/envs/cdvae/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 844, in run_train
self.run_sanity_check(self.lightning_module)
File "/Users/wiltonkortkamp/opt/anaconda3/envs/cdvae/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1112, in run_sanity_check
self.run_evaluation()
File "/Users/wiltonkortkamp/opt/anaconda3/envs/cdvae/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 967, in run_evaluation
output = self.evaluation_loop.evaluation_step(batch, batch_idx, dataloader_idx)
File "/Users/wiltonkortkamp/opt/anaconda3/envs/cdvae/lib/python3.8/site-packages/pytorch_lightning/trainer/evaluation_loop.py", line 174, in evaluation_step
output = self.trainer.accelerator.validation_step(args)
File "/Users/wiltonkortkamp/opt/anaconda3/envs/cdvae/lib/python3.8/site-packages/pytorch_lightning/accelerators/accelerator.py", line 226, in validation_step
return self.training_type_plugin.validation_step(*args)
File "/Users/wiltonkortkamp/opt/anaconda3/envs/cdvae/lib/python3.8/site-packages/pytorch_lightning/plugins/training_type/training_type_plugin.py", line 161, in validation_step
return self.lightning_module.validation_step(*args, **kwargs)
File "/Users/wiltonkortkamp/opt/anaconda3/envs/cdvae/lib/python3.8/site-packages/pytorch_lightning/plugins/precision/double.py", line 54, in new_method
return old_method(
File "/Users/wiltonkortkamp/Downloads/cdvae-main/cdvae/pl_modules/model.py", line 539, in validation_step
outputs = self(batch, teacher_forcing=False, training=False)
File "/Users/wiltonkortkamp/opt/anaconda3/envs/cdvae/lib/python3.8/site-packages/torch/nn/modules/module.py", line 889, in _call_impl
result = self.forward(*input, **kwargs)
File "/Users/wiltonkortkamp/opt/anaconda3/envs/cdvae/lib/python3.8/site-packages/pytorch_lightning/plugins/precision/double.py", line 54, in new_method
return old_method(
File "/Users/wiltonkortkamp/Downloads/cdvae-main/cdvae/pl_modules/model.py", line 312, in forward
mu, log_var, z = self.encode(batch)
File "/Users/wiltonkortkamp/Downloads/cdvae-main/cdvae/pl_modules/model.py", line 198, in encode
hidden = self.encoder(batch)
File "/Users/wiltonkortkamp/opt/anaconda3/envs/cdvae/lib/python3.8/site-packages/torch/nn/modules/module.py", line 889, in _call_impl
result = self.forward(*input, **kwargs)
File "/Users/wiltonkortkamp/Downloads/cdvae-main/cdvae/pl_modules/gnn.py", line 354, in forward
pos = frac_to_cart_coords(
File "/Users/wiltonkortkamp/Downloads/cdvae-main/cdvae/common/data_utils.py", line 253, in frac_to_cart_coords
pos = torch.einsum('bi,bij->bj', frac_coords, lattice_nodes) # cart coords
File "/Users/wiltonkortkamp/opt/anaconda3/envs/cdvae/lib/python3.8/site-packages/torch/functional.py", line 408, in einsum
return _VF.einsum(equation, operands) # type: ignore
RuntimeError: expected scalar type Float but found Double

Missing/sparse code documentation (i.e. docstrings)

e.g. https://github.com/txie-93/cdvae/blob/main/scripts/compute_metrics.py

Environment issues and some name inconsistency

Dear authors,

Hi, when I install the environment by running 'conda env create -f env.yml', I got stuck in the 'solving environment' step of conda.

Could you check this step? I think something might be wrong here ~.

Also, there are some errors when I run the training code. Some places you name it cdvae but you use crystalvae to refer it in the pipeline.

Instructions for use of the benchmark datasets and metrics on custom generative models

Hi @txie-93, I'm enjoying digging into the manuscript, and congratulations on its acceptance to ICLR! It is really nice to see the comparison with FTCP and other methods, and CDVAE certainly has some impressive results.

Would you mind providing some instructions in the repository for using the benchmark datasets and the metrics on a custom generative model? For example, how would this look for FTCP or the slew of other generative models in this space (i.e. the general inverse design ones)?

How can I solve the cuda capability problem?

When I command 'python cdvae/run.py data=perov expname=perov',I meet the problem that CUDA capabilities probelm.The concrete hint is as follow:

packages/torch/cuda/__init__.py:104: UserWarning: 
NVIDIA GeForce RTX 4090 with CUDA capability sm_89 is not compatible with the current PyTorch installation.
The current PyTorch install supports CUDA capabilities sm_37 sm_50 sm_60 sm_61 sm_70 sm_75 compute_37.

Consider using ElMD for the composition-based distance

For a "more chemically intuitive" distance metric. Original implementation: ElMD and ElM2D. Lightning-fast version of ElM2D at https://github.com/sparks-baird/chem_wasserstein. $10k \times 10k$ pairwise distances on order of ~10 seconds on CPU.

cdvae/scripts/compute_metrics.py

Line 17 in f857f59

from matminer.featurizers.composition.composite import ElementProperty

cdvae/scripts/compute_metrics.py

Line 92 in f857f59

self.comp_fp = CompFP.featurize(comp)

What is the version/commit that was used to produce the paper results?

For help in troubleshooting getting it up and running.

successful env.cpu.yml

channels:

pytorch
conda-forge
defaults
pyg
dependencies:
ase=3.22
autopep8
cpuonly
ipywidgets
jupyterlab
matminer
matplotlib
nglview
pip
pyg=2.0.1
pylint
pymatgen
python=3.8.*
pytorch-lightning=1.3.8
pytorch=1.8.1
seaborn
tqdm
monty=2022.4.26
torchmetrics=0.5.0
ruamel.yaml=0.16.6
pip:
- hydra-core==1.1.0
- hydra-joblib-launcher==1.1.5
- p-tqdm==1.3.3
- pytest
- python-dotenv
- smact==2.2.1
- streamlit==0.79.0
- wandb==0.10.33
  name: cdvae

Working CDVAE implementation with Dockerfile (CUDA major versions 10, 11, 12)

Hi,

I plan to submit a PR ultimately, but wanted to get this out there as soon as possible. I had significant problems getting the provided miniconda configuration working correctly. After manually cowboy-engineering many of the dependencies, I obtained a workable dependency collection but found several areas of the source code were themselves problematic.

A colleague of mine updated the source code and sent me a tarball. I've published this updated source code and provided a Dockerfile and build instructions for running CDVAE (tested on major CUDA versions 10, 11, 12).

Hope this helps others attempting to reproduce these results...

Link: https://github.com/njkrichardson/cdvae/tree/master

ModuleNotFoundError: No module named 'cdvae'

(cdvae) abid@lab3021:~/cdvae$ python cdvae/run.py data=perov expname=perov

Bad key "text.kerning_factor" on line 4 in
/home/abid/anaconda3/envs/cdvae/lib/python3.8/site-packages/matplotlib/mpl-data/stylelib/_classic_test_patch.mplstyle.
You probably need to get an updated matplotlibrc file from
https://github.com/matplotlib/matplotlib/blob/v3.1.2/matplotlibrc.template
or from the matplotlib source distribution
Traceback (most recent call last):
File "cdvae/run.py", line 19, in
from cdvae.common.utils import log_hyperparameters, PROJECT_ROOT
ModuleNotFoundError: No module named `'cdvae'```

Does anyone have the solution of this problem?

pytorch_lightning.utilities.exceptions.MisconfigurationException: ReduceLROnPlateau conditioned on metric val_loss

  | Name           | Type                | Params
-------------------------------------------------------
0 | encoder        | DimeNetPlusPlusWrap | 2.2 M 
1 | decoder        | GemNetTDecoder      | 2.3 M 
2 | fc_mu          | Linear              | 65.8 K
3 | fc_var         | Linear              | 65.8 K
4 | fc_num_atoms   | Sequential          | 71.2 K
5 | fc_lattice     | Sequential          | 67.3 K
6 | fc_composition | Sequential          | 91.5 K
-------------------------------------------------------
4.9 M     Trainable params
123       Non-trainable params
4.9 M     Total params
19.682    Total estimated model params size (MB)
/home2/harsha.vasamsetti/miniconda3/envs/cdvae/lib/python3.8/site-packages/pytorch_lightning/callbacks/model_checkpoint.py:631: UserWarning: Checkpoint directory /scratch/harsha.vasamsetti/hydra/singlerun/2023-05-26/perov exists and is not empty.
  rank_zero_warn(f"Checkpoint directory {dirpath} exists and is not empty.")
Validation sanity check: 0it [00:00, ?it/s]/home2/harsha.vasamsetti/miniconda3/envs/cdvae/lib/python3.8/site-packages/torch_geometric/deprecation.py:13: UserWarning: 'data.DataLoader' is deprecated, use 'loader.DataLoader' instead
  warnings.warn(out)
/home2/harsha.vasamsetti/miniconda3/envs/cdvae/lib/python3.8/site-packages/pytorch_lightning/trainer/data_loading.py:116: UserWarning: The dataloader, val_dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 40 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.
  rank_zero_warn(
Validation sanity check:   0%|                                                                | 0/2 [00:00<?, ?it/s]/scratch/harsha.vasamsetti/cdvae/cdvae/common/data_utils.py:622: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  X = torch.tensor(X, dtype=torch.float)
/scratch/harsha.vasamsetti/cdvae/cdvae/common/data_utils.py:618: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  X = torch.tensor(X, dtype=torch.float)
/home2/harsha.vasamsetti/miniconda3/envs/cdvae/lib/python3.8/site-packages/pytorch_lightning/utilities/data.py:59: UserWarning: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 10. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`.
  warning_cache.warn(
/home2/harsha.vasamsetti/miniconda3/envs/cdvae/lib/python3.8/site-packages/pytorch_lightning/trainer/data_loading.py:116: UserWarning: The dataloader, train_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 40 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.
  rank_zero_warn(
/home2/harsha.vasamsetti/miniconda3/envs/cdvae/lib/python3.8/site-packages/pytorch_lightning/trainer/data_loading.py:412: UserWarning: The number of training samples (23) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.
  rank_zero_warn(
Epoch 0: 100%|█| 23/23 [00:09<00:00,  2.53it/s, loss=91.2, v_num=t2wv, train_loss_step=80.70, train_natom_loss_step=Error executing job with overrides: ['data=perov', 'expname=perov']
Traceback (most recent call last):
  File "cdvae/run.py", line 167, in main
    run(cfg)
  File "cdvae/run.py", line 155, in run
    trainer.fit(model=model, datamodule=datamodule)
  File "/home2/harsha.vasamsetti/miniconda3/envs/cdvae/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 738, in fit
    self._call_and_handle_interrupt(
  File "/home2/harsha.vasamsetti/miniconda3/envs/cdvae/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 683, in _call_and_handle_interrupt
    return trainer_fn(*args, **kwargs)
  File "/home2/harsha.vasamsetti/miniconda3/envs/cdvae/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 773, in _fit_impl
    self._run(model, ckpt_path=ckpt_path)
  File "/home2/harsha.vasamsetti/miniconda3/envs/cdvae/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1195, in _run
    self._dispatch()
  File "/home2/harsha.vasamsetti/miniconda3/envs/cdvae/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1275, in _dispatch
    self.training_type_plugin.start_training(self)
  File "/home2/harsha.vasamsetti/miniconda3/envs/cdvae/lib/python3.8/site-packages/pytorch_lightning/plugins/training_type/training_type_plugin.py", line 202, in start_training
    self._results = trainer.run_stage()
  File "/home2/harsha.vasamsetti/miniconda3/envs/cdvae/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1285, in run_stage
    return self._run_train()
  File "/home2/harsha.vasamsetti/miniconda3/envs/cdvae/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1315, in _run_train
    self.fit_loop.run()
  File "/home2/harsha.vasamsetti/miniconda3/envs/cdvae/lib/python3.8/site-packages/pytorch_lightning/loops/base.py", line 145, in run
    self.advance(*args, **kwargs)
  File "/home2/harsha.vasamsetti/miniconda3/envs/cdvae/lib/python3.8/site-packages/pytorch_lightning/loops/fit_loop.py", line 234, in advance
    self.epoch_loop.run(data_fetcher)
  File "/home2/harsha.vasamsetti/miniconda3/envs/cdvae/lib/python3.8/site-packages/pytorch_lightning/loops/base.py", line 151, in run
    output = self.on_run_end()
  File "/home2/harsha.vasamsetti/miniconda3/envs/cdvae/lib/python3.8/site-packages/pytorch_lightning/loops/epoch/training_epoch_loop.py", line 303, in on_run_end
    self.update_lr_schedulers("epoch", update_plateau_schedulers=True)
  File "/home2/harsha.vasamsetti/miniconda3/envs/cdvae/lib/python3.8/site-packages/pytorch_lightning/loops/epoch/training_epoch_loop.py", line 441, in update_lr_schedulers
    self._update_learning_rates(
  File "/home2/harsha.vasamsetti/miniconda3/envs/cdvae/lib/python3.8/site-packages/pytorch_lightning/loops/epoch/training_epoch_loop.py", line 486, in _update_learning_rates
    raise MisconfigurationException(
pytorch_lightning.utilities.exceptions.MisconfigurationException: ReduceLROnPlateau conditioned on metric val_loss which is not available. Available metrics are: ['train_loss', 'train_loss_step', 'train_natom_loss', 'train_natom_loss_step', 'train_lattice_loss', 'train_lattice_loss_step', 'train_coord_loss', 'train_coord_loss_step', 'train_type_loss', 'train_type_loss_step', 'train_kld_loss', 'train_kld_loss_step', 'train_composition_loss', 'train_composition_loss_step', 'train_loss_epoch', 'train_natom_loss_epoch', 'train_lattice_loss_epoch', 'train_coord_loss_epoch', 'train_type_loss_epoch', 'train_kld_loss_epoch', 'train_composition_loss_epoch']. Condition can be set using `monitor` key in lr scheduler dict

When training the code, I am receiving this error.

Reconstruction evaluation error

Hello @txie-93!

Thanks for the great code/repo. It is refreshing to have such high quality research code.

I have trained a model with property prediction, but running the evaluation for reconstruction throws this error:

(cdvae) markn@MacBook-Pro cdvae % PYTHONPATH=. python scripts/evaluate.py --model_path /Users/markn/code/cdvae/hydra/singlerun/2022-09-15/mini-property-pred --tasks gen opt recon --batch_size 10 --num_batches_to_samples 2
/Users/markn/anaconda3/envs/cdvae/lib/python3.8/site-packages/hydra/experimental/compose.py:16: UserWarning: hydra.experimental.compose() is no longer experimental. Use hydra.compose()
  warnings.warn(
 72%|██████████████████████████████████████████████████████▋                     | 285/396 [00:17<00:05, 20.42it/s]/Users/markn/anaconda3/envs/cdvae/lib/python3.8/site-packages/pymatgen/io/cif.py:1193: UserWarning: Issues encountered while parsing CIF: Some fractional co-ordinates rounded to ideal values to avoid issues with finite precision.
  warnings.warn(
100%|████████████████████████████████████████████████████████████████████████████| 396/396 [00:24<00:00, 16.12it/s]
/Users/markn/anaconda3/envs/cdvae/lib/python3.8/site-packages/torch_geometric/deprecation.py:13: UserWarning: 'data.DataLoader' is deprecated, use 'loader.DataLoader' instead
  warnings.warn(out)
Evaluate model on the reconstruction task.
batch 0 in 13
/Users/markn/code/cdvae/cdvae/common/data_utils.py:622: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  X = torch.tensor(X, dtype=torch.float)
100%|██████████████████████████████████████████████████████████████████████████████| 50/50 [10:50<00:00, 13.01s/it]
Batch(edge_index=[2, 1114], y=[32, 1], frac_coords=[298, 3], atom_types=[298], lengths=[32, 3], angles=[32, 3], to_jimages=[1114, 3], num_atoms=[32], num_bonds=[32], num_nodes=298, batch=[298], ptr=[33])
Traceback (most recent call last):
  File "scripts/evaluate.py", line 281, in <module>
    main(args)
  File "scripts/evaluate.py", line 195, in main
    all_frac_coords_stack, all_atom_types_stack, input_data_batch) = reconstructon(
  File "scripts/evaluate.py", line 70, in reconstructon
    input_data_list = input_data_list + batch.to_data_list()
  File "/Users/markn/anaconda3/envs/cdvae/lib/python3.8/site-packages/torch_geometric/data/batch.py", line 157, in to_data_list
    return [self.get(i) for i in range(self.num_graphs)]
  File "/Users/markn/anaconda3/envs/cdvae/lib/python3.8/site-packages/torch_geometric/data/batch.py", line 157, in <listcomp>
    return [self.get(i) for i in range(self.num_graphs)]
  File "/Users/markn/anaconda3/envs/cdvae/lib/python3.8/site-packages/torch_geometric/data/batch.py", line 90, in get
    data = separate(
  File "/Users/markn/anaconda3/envs/cdvae/lib/python3.8/site-packages/torch_geometric/data/separate.py", line 40, in separate
    data_store[attr] = _separate(attr, batch_store[attr], idx, slices,
  File "/Users/markn/anaconda3/envs/cdvae/lib/python3.8/site-packages/torch_geometric/data/separate.py", line 87, in _separate
    if decrement and (incs.dim() > 1 or int(incs[idx]) != 0):
AttributeError: 'NoneType' object has no attribute 'dim'

Perhaps this is related to the pytorch geometric version? The version that was installed is torch-geometric==2.0.1

Otherwise, do you have any pointers/suggestions?

thanks,

Mark

Has anyone else managed to reproduce the paper results?

Error when doing training: Segmentation fault

Hi professor, I followed the installation in README and use env.yaml to create the conda environment on GPU clusters. When i try command like python cdvae/run.py data=carbon an error occurs:
[2022-12-15 21:18:19,363][hydra.utils][INFO] - Instantiating <cdvae.pl_data.datamodule.CrystDataModule> Segmentation fault (core dumped)
I have tried other data like mp_20 and tried to add CXX=g++ before command but still failed. Could you please help me about that? Thanks !

Using your evaluation script (compute_metrics.py)

Hi, thanks for the repository.

I wanted to use your metrics script to evaluate generated structures from mp_20 dataset. I have the training dataset and the generated dataset both in .cif format. I wish to ask, how may I use your script to evaluate the performance of my model?

As per my understanding we need of compute_metrics.py in order to get the crystal list we need to convert the .cif structure to a data dictionary with keys : {frac_coords, atom_types, lengths, angles, num_atoms}.

How to get these values for the above keys from my .cif file ?

compute_metrics.py :

cdvae/scripts/compute_metrics.py

Line 267 in f857f59

def get_crystal_array_list(file_path, batch_idx=0):

def get_crystal_array_list(file_path, batch_idx=0):

    data = load_data(file_path)

    crys_array_list = get_crystals_list(
        data['frac_coords'][batch_idx],
        data['atom_types'][batch_idx],
        data['lengths'][batch_idx],
        data['angles'][batch_idx],
        data['num_atoms'][batch_idx])

What is the shape of the graph network input layer that represents a single crystal structure?

I believe CGCNN is a 3D matrix that looks more like a cube than a high-aspect ratio box, though I don't remember the specific default shape off the top of my head. Assuming this is a valid question to be asking, what is the input shape that represents a single crystal structure in CDVAE? E.g. $100\times100\times50$.

Underscore vs. hyphen in cli arg `mp_20` correct, `mp-20` not

Not a big deal, but could add mp-20, perov-20, and carbon-20 in addition to the underscored versions for data=...

Any interest in a PR for installation via pip and conda?

If so, happy to submit a PR

Code Hanging At Start of Training

Hi, I am running the CDVAE carbon experiment and I have been seeing a weird error. It appears that my code will just hang after completely three iterations of the first epoch.

I run **python cdvae/run.py data=carbon expname=carbon model.predict_property=True**

The output I see is this:

`[2023-07-13 16:57:36,190][hydra.utils][INFO] - Instantiating <cdvae.pl_data.datamodule.CrystDataModule>
[2023-07-13 16:57:37,161][numexpr.utils][INFO] - Note: detected 128 virtual cores but NumExpr set to maximum of 64, check "NUMEXPR_MAX_THREADS" environment variable.
[2023-07-13 16:57:37,161][numexpr.utils][INFO] - Note: NumExpr detected 128 cores but "NUMEXPR_MAX_THREADS" not set, so enforcing safe limit of 8.
 25%|█████████████████████▍                                                                | 1521/6091 [00:25<01:29, 50.81it/s]/home/.conda/envs/cdvae/lib/python3.8/site-packages/pymatgen/io/cif.py:1120: UserWarning: Issues encountered while parsing CIF: Some fractional coordinates rounded to ideal values to avoid issues with finite precision.
  warnings.warn("Issues encountered while parsing CIF: " + "\n".join(self.warnings))
 34%|█████████████████████████████▎                                                        | 2080/6091 [00:34<01:05, 61.51it/s]/home/.conda/envs/cdvae/lib/python3.8/site-packages/pymatgen/io/cif.py:1120: UserWarning: Issues encountered while parsing CIF: Some fractional coordinates rounded to ideal values to avoid issues with finite precision.
  warnings.warn("Issues encountered while parsing CIF: " + "\n".join(self.warnings))
 46%|███████████████████████████████████████▊                                              | 2820/6091 [00:46<01:02, 52.70it/s]/home/.conda/envs/cdvae/lib/python3.8/site-packages/pymatgen/io/cif.py:1120: UserWarning: Issues encountered while parsing CIF: Some fractional coordinates rounded to ideal values to avoid issues with finite precision.
  warnings.warn("Issues encountered while parsing CIF: " + "\n".join(self.warnings))
 50%|██████████████████████████████████████████▋                                           | 3021/6091 [00:49<00:52, 58.26it/s]/home/.conda/envs/cdvae/lib/python3.8/site-packages/pymatgen/io/cif.py:1120: UserWarning: Issues encountered while parsing CIF: Some fractional coordinates rounded to ideal values to avoid issues with finite precision.
  warnings.warn("Issues encountered while parsing CIF: " + "\n".join(self.warnings))
 51%|███████████████████████████████████████████▍                                          | 3079/6091 [00:50<00:54, 55.41it/s]/home/.conda/envs/cdvae/lib/python3.8/site-packages/pymatgen/io/cif.py:1120: UserWarning: Issues encountered while parsing CIF: Some fractional coordinates rounded to ideal values to avoid issues with finite precision.
  warnings.warn("Issues encountered while parsing CIF: " + "\n".join(self.warnings))
 51%|████████████████████████████████████████████▏                                         | 3132/6091 [00:51<00:40, 72.78it/s]/home/.conda/envs/cdvae/lib/python3.8/site-packages/pymatgen/io/cif.py:1120: UserWarning: Issues encountered while parsing CIF: Some fractional coordinates rounded to ideal values to avoid issues with finite precision.
  warnings.warn("Issues encountered while parsing CIF: " + "\n".join(self.warnings))
 52%|████████████████████████████████████████████▎                                         | 3140/6091 [00:51<00:49, 59.77it/s]/home/.conda/envs/cdvae/lib/python3.8/site-packages/pymatgen/io/cif.py:1120: UserWarning: Issues encountered while parsing CIF: Some fractional coordinates rounded to ideal values to avoid issues with finite precision.
  warnings.warn("Issues encountered while parsing CIF: " + "\n".join(self.warnings))
 60%|███████████████████████████████████████████████████▊                                  | 3673/6091 [00:59<00:38, 63.39it/s]/home/.conda/envs/cdvae/lib/python3.8/site-packages/pymatgen/io/cif.py:1120: UserWarning: Issues encountered while parsing CIF: Some fractional coordinates rounded to ideal values to avoid issues with finite precision.
  warnings.warn("Issues encountered while parsing CIF: " + "\n".join(self.warnings))
 66%|████████████████████████████████████████████████████████▋                             | 4018/6091 [01:05<00:32, 63.75it/s]/home/.conda/envs/cdvae/lib/python3.8/site-packages/pymatgen/io/cif.py:1120: UserWarning: Issues encountered while parsing CIF: Some fractional coordinates rounded to ideal values to avoid issues with finite precision.
  warnings.warn("Issues encountered while parsing CIF: " + "\n".join(self.warnings))
 67%|█████████████████████████████████████████████████████████▌                            | 4077/6091 [01:06<00:33, 60.74it/s]/home/.conda/envs/cdvae/lib/python3.8/site-packages/pymatgen/io/cif.py:1120: UserWarning: Issues encountered while parsing CIF: Some fractional coordinates rounded to ideal values to avoid issues with finite precision.
  warnings.warn("Issues encountered while parsing CIF: " + "\n".join(self.warnings))
 67%|█████████████████████████████████████████████████████████▊                            | 4098/6091 [01:06<00:29, 67.92it/s]/home/.conda/envs/cdvae/lib/python3.8/site-packages/pymatgen/io/cif.py:1120: UserWarning: Issues encountered while parsing CIF: Some fractional coordinates rounded to ideal values to avoid issues with finite precision.
  warnings.warn("Issues encountered while parsing CIF: " + "\n".join(self.warnings))
 69%|███████████████████████████████████████████████████████████▊                          | 4233/6091 [01:08<00:29, 63.53it/s]/home/.conda/envs/cdvae/lib/python3.8/site-packages/pymatgen/io/cif.py:1120: UserWarning: Issues encountered while parsing CIF: Some fractional coordinates rounded to ideal values to avoid issues with finite precision.
  warnings.warn("Issues encountered while parsing CIF: " + "\n".join(self.warnings))
 74%|████████████████████████████████████████████████████████████████                      | 4536/6091 [01:13<00:23, 67.50it/s]/home/.conda/envs/cdvae/lib/python3.8/site-packages/pymatgen/io/cif.py:1120: UserWarning: Issues encountered while parsing CIF: Some fractional coordinates rounded to ideal values to avoid issues with finite precision.
  warnings.warn("Issues encountered while parsing CIF: " + "\n".join(self.warnings))
 80%|████████████████████████████████████████████████████████████████████▋                 | 4869/6091 [01:18<00:16, 72.17it/s]/home/.conda/envs/cdvae/lib/python3.8/site-packages/pymatgen/io/cif.py:1120: UserWarning: Issues encountered while parsing CIF: Some fractional coordinates rounded to ideal values to avoid issues with finite precision.
  warnings.warn("Issues encountered while parsing CIF: " + "\n".join(self.warnings))
 84%|████████████████████████████████████████████████████████████████████████              | 5106/6091 [01:22<00:18, 53.65it/s]/home/.conda/envs/cdvae/lib/python3.8/site-packages/pymatgen/io/cif.py:1120: UserWarning: Issues encountered while parsing CIF: Some fractional coordinates rounded to ideal values to avoid issues with finite precision.
  warnings.warn("Issues encountered while parsing CIF: " + "\n".join(self.warnings))
 91%|██████████████████████████████████████████████████████████████████████████████▌       | 5566/6091 [01:29<00:08, 63.96it/s]/home/.conda/envs/cdvae/lib/python3.8/site-packages/pymatgen/io/cif.py:1120: UserWarning: Issues encountered while parsing CIF: Some fractional coordinates rounded to ideal values to avoid issues with finite precision.
  warnings.warn("Issues encountered while parsing CIF: " + "\n".join(self.warnings))
 95%|█████████████████████████████████████████████████████████████████████████████████▋    | 5786/6091 [01:33<00:05, 59.95it/s]/home/.conda/envs/cdvae/lib/python3.8/site-packages/pymatgen/io/cif.py:1120: UserWarning: Issues encountered while parsing CIF: Some fractional coordinates rounded to ideal values to avoid issues with finite precision.
  warnings.warn("Issues encountered while parsing CIF: " + "\n".join(self.warnings))
 96%|██████████████████████████████████████████████████████████████████████████████████▏   | 5822/6091 [01:33<00:04, 64.72it/s]/home/.conda/envs/cdvae/lib/python3.8/site-packages/pymatgen/io/cif.py:1120: UserWarning: Issues encountered while parsing CIF: Some fractional coordinates rounded to ideal values to avoid issues with finite precision.
  warnings.warn("Issues encountered while parsing CIF: " + "\n".join(self.warnings))
 98%|████████████████████████████████████████████████████████████████████████████████████▎ | 5974/6091 [01:36<00:01, 66.91it/s]/home/.conda/envs/cdvae/lib/python3.8/site-packages/pymatgen/io/cif.py:1120: UserWarning: Issues encountered while parsing CIF: Some fractional coordinates rounded to ideal values to avoid issues with finite precision.
  warnings.warn("Issues encountered while parsing CIF: " + "\n".join(self.warnings))
100%|██████████████████████████████████████████████████████████████████████████████████████| 6091/6091 [01:39<00:00, 61.48it/s]
/gpfs/fs1/home/cdvae-old/cdvae/cdvae/common/data_utils.py:644: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at  /data/miniconda3/envs/opence-1.7/conda-bld/pytorch-base_1663986328871/work/torch/csrc/utils/tensor_new.cpp:201.)
  targets = torch.tensor([d[key] for d in data_list])
/gpfs/fs1/home/cdvae-old/cdvae/cdvae/common/data_utils.py:612: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  X = torch.tensor(X, dtype=torch.float)
[2023-07-13 16:59:20,540][hydra.utils][INFO] - Instantiating <cdvae.pl_modules.model.CDVAE>
[2023-07-13 16:59:20,615][torch.distributed.nn.jit.instantiator][INFO] - Created a temporary directory at /tmp/tmpwv1glt9u
[2023-07-13 16:59:20,615][torch.distributed.nn.jit.instantiator][INFO] - Writing /tmp/tmpwv1glt9u/_remote_module_non_scriptable.py
[2023-07-13 16:59:53,346][hydra.utils][INFO] - Passing scaler from datamodule to model <StandardScalerTorch(means: -154.2510223388672, stds: 0.13738815486431122)>
[2023-07-13 16:59:53,348][hydra.utils][INFO] - Adding callback <LearningRateMonitor>
[2023-07-13 16:59:53,349][hydra.utils][INFO] - Adding callback <EarlyStopping>
[2023-07-13 16:59:53,350][hydra.utils][INFO] - Adding callback <ModelCheckpoint>
[2023-07-13 16:59:53,354][hydra.utils][INFO] - Instantiating <WandbLogger>
wandb: Currently logged in as: _. Use `wandb login --relogin` to force relogin
wandb: Tracking run with wandb version 0.15.5
wandb: Run data is saved locally in /home/cdvae-old/cdvae/wabdb/wandb/run-20230713_165954-u04zv43g
wandb: Run `wandb offline` to turn off syncing.
wandb: Syncing run carbon
wandb: ⭐️ View project at https://wandb.ai/_/crystal_generation_mit
wandb: 🚀 View run at https://wandb.ai/_/crystal_generation_mit/runs/u04zv43g
[2023-07-13 17:00:07,550][hydra.utils][INFO] - W&B is now watching <{cfg.logging.wandb_watch.log}>!
wandb: logging graph, to disable use `wandb.watch(log_graph=False)`
[2023-07-13 17:00:07,588][hydra.utils][INFO] - Instantiating the Trainer
/home/.conda/envs/cdvae/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/callback_connector.py:96: LightningDeprecationWarning: Setting `Trainer(progress_bar_refresh_rate=20)` is deprecated in v1.5 and will be removedin v1.7. Please pass `pytorch_lightning.callbacks.progress.TQDMProgressBar` with `refresh_rate` directly to the Trainer's `callbacks` argument instead. Or, to disable the progress bar pass `enable_progress_bar = False` to the Trainer.
  rank_zero_deprecation(
GPU available: True, used: True
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
[2023-07-13 17:00:07,650][hydra.utils][INFO] - Starting training!
  0%|                                                                                         | 2/6091 [00:00<32:19,  3.14it/s

I am running on MIST HPC, so I have turned off WandB logging.

Environment

Package                  Version           Editable project location
------------------------ ----------------- --------------------------------------------------
absl-py                  1.4.0
aiofiles                 22.1.0
aiohttp                  3.8.4
aiosignal                1.3.1
aiosqlite                0.18.0
altair                   5.0.1
antlr4-python3-runtime   4.8
anyio                    3.5.0
appdirs                  1.4.4
argon2-cffi              21.3.0
argon2-cffi-bindings     21.2.0
ase                      3.22.0
astor                    0.8.1
astroid                  2.14.2
asttokens                2.0.5
async-timeout            4.0.2
attrs                    22.1.0
autopep8                 2.0.2
av                       9.2.0
Babel                    2.11.0
backcall                 0.2.0
backports.zoneinfo       0.2.1
base58                   2.1.1
beautifulsoup4           4.12.2
bleach                   4.1.0
blinker                  1.6.2
Bottleneck               1.3.5
brotlipy                 0.7.0
cachetools               5.3.1
cdvae                    0.0.1             
certifi                  2023.5.7
cffi                     1.15.1
charset-normalizer       2.0.4
click                    8.0.4
colorama                 0.4.6
comm                     0.1.2
configparser             6.0.0
contourpy                1.0.5
coverage                 7.2.2
cryptography             39.0.1
cycler                   0.11.0
debugpy                  1.5.1
decorator                5.1.1
defusedxml               0.7.1
dill                     0.3.6
distlib                  0.3.6
dnspython                2.3.0
docker-pycreds           0.4.0
emmet-core               0.60.1
entrypoints              0.4
exceptiongroup           1.0.4
executing                0.8.3
fastjsonschema           2.16.2
filelock                 3.12.0
fonttools                4.25.0
frozenlist               1.3.3
fsspec                   2023.4.0
future                   0.18.3
gitdb                    4.0.10
GitPython                3.1.32
google-auth              2.22.0
google-auth-oauthlib     1.0.0
googledrivedownloader    0.4
grpcio                   1.48.2
higher                   0.2.1
html5lib                 1.1
hydra-core               1.1.0
hydra-joblib-launcher    1.1.5
idna                     3.4
importlib-metadata       6.0.0
importlib-resources      5.12.0
iniconfig                1.1.1
ipykernel                6.19.2
ipython                  8.12.0
ipython-genutils         0.2.0
ipywidgets               8.0.4
isodate                  0.6.1
isort                    5.9.3
jedi                     0.18.1
Jinja2                   3.1.2
joblib                   1.2.0
json5                    0.9.6
jsonschema               4.17.3
jupyter_client           8.1.0
jupyter_core             5.3.0
jupyter-events           0.6.3
jupyter_server           2.5.0
jupyter_server_fileid    0.9.0
jupyter_server_terminals 0.4.4
jupyter_server_ydoc      0.8.0
jupyter-ydoc             0.2.4
jupyterlab               3.6.3
jupyterlab-pygments      0.1.2
jupyterlab_server        2.22.0
jupyterlab-widgets       3.0.5
kiwisolver               1.4.4
latexcodec               2.0.1
lazy-object-proxy        1.6.0
lightning-utilities      0.7.1
lxml                     4.9.2
Markdown                 3.4.3
MarkupSafe               2.1.1
matminer                 0.7.3
matplotlib               3.7.1
matplotlib-inline        0.1.6
mccabe                   0.7.0
mistune                  0.8.4
monty                    2023.5.8
mp-api                   0.33.3
mpmath                   1.3.0
msgpack                  1.0.5
multidict                6.0.4
multiprocess             0.70.14
munkres                  1.1.4
nbclassic                0.5.5
nbclient                 0.5.13
nbconvert                6.5.4
nbformat                 5.7.0
nest-asyncio             1.5.6
networkx                 2.8.4
nglview                  3.0.6
notebook                 6.5.4
notebook_shim            0.2.2
numexpr                  2.8.4
numpy                    1.23.5
oauthlib                 3.2.2
omegaconf                2.1.2
p-tqdm                   1.3.3
packaging                23.0
palettable               3.3.3
pandas                   1.5.3
pandocfilters            1.5.0
parso                    0.8.3
pathos                   0.3.0
pathtools                0.1.2
pexpect                  4.8.0
pickleshare              0.7.5
Pillow                   9.4.0
Pint                     0.21.1
pip                      23.1.2
pkgutil_resolve_name     1.3.10
platformdirs             3.2.0
plotly                   5.15.0
pluggy                   1.0.0
pox                      0.3.2
ppft                     1.7.6.6
prometheus-client        0.14.1
promise                  2.3
prompt-toolkit           3.0.36
protobuf                 3.19.6
psutil                   5.9.0
ptyprocess               0.7.0
pure-eval                0.2.2
py                       1.11.0
pyarrow                  8.0.0
pyasn1                   0.5.0
pyasn1-modules           0.3.0
pybtex                   0.24.0
pycodestyle              2.10.0
pycparser                2.21
pydantic                 1.10.11
pydeck                   0.8.1b0
pyDeprecate              0.3.1
pyg-nightly              2.4.0.dev20230711
Pygments                 2.15.1
pylint                   2.16.2
pymatgen                 2023.7.11
pymongo                  4.4.0
pyOpenSSL                23.0.0
pyparsing                3.0.9
pyrsistent               0.18.0
PySocks                  1.7.1
pytest                   7.3.1
pytest-cov               4.0.0
python-dateutil          2.8.2
python-dotenv            1.0.0
python-json-logger       2.0.7
python-louvain           0.15
pytorch-lightning        1.6.5
pytz                     2022.7
PyYAML                   5.4.1
pyzmq                    25.1.0
rdflib                   6.1.1
requests                 2.29.0
requests-oauthlib        1.3.1
rfc3339-validator        0.1.4
rfc3986-validator        0.1.1
rsa                      4.9
ruamel.yaml              0.17.32
ruamel.yaml.clib         0.2.7
scikit-learn             1.2.2
scipy                    1.8.1
Send2Trash               1.8.0
sentencepiece            0.1.96
sentry-sdk               1.28.0
setproctitle             1.3.2
setuptools               67.8.0
shortuuid                1.0.11
six                      1.16.0
SMACT                    2.2.1
smmap                    5.0.0
sniffio                  1.2.0
soupsieve                2.4
spglib                   2.0.2
stack-data               0.2.0
streamlit                0.79.0
subprocess32             3.5.4
sympy                    1.12
tabulate                 0.8.10
tenacity                 8.2.2
tensorboard              2.13.0
tensorboard-data-server  0.7.1
terminado                0.17.1
threadpoolctl            2.2.0
tinycss2                 1.2.1
toml                     0.10.2
tomli                    2.0.1
tomlkit                  0.11.1
toolz                    0.12.0
torch                    1.12.1
torch-cluster            1.6.1
torch-geometric          1.7.2
torch-scatter            2.0.8
torch-sparse             0.6.10
torch-spline-conv        1.2.2
torchdiffeq              0.0.1
torchmetrics             1.0.0
torchtext                0.13.1a0+35066f2
torchvision              0.13.1
tornado                  6.2
tqdm                     4.65.0
traitlets                5.7.1
typing_extensions        4.6.3
tzlocal                  5.0.1
uncertainties            3.1.7
urllib3                  1.26.16
validators               0.20.0
virtualenv               20.22.0
wandb                    0.15.5
watchdog                 3.0.0
wcwidth                  0.2.5
webencodings             0.5.1
websocket-client         0.58.0
Werkzeug                 2.3.6
wheel                    0.38.4
widgetsnbextension       4.0.5
wrapt                    1.14.1
y-py                     0.5.9
yacs                     0.1.6
yarl                     1.9.2
ypy-websocket            0.8.2
zipp                     3.11.0

Any suggestions on how to resolve this? I am not very familiar with Hydra and Pytorch Lightning.

How to train prop_models on a custom dataset

I really appreciate your work and I want to train and evaluate this model on a custom dataset. However, after completing the three tasks, I failed to evaluate the generation task and optimisation task. It seems that I need to train another property predictor (like those models in cdvae/prop_models) on the custom dataset independently, but I failed find the code to train it. Would you mind provide some instructions to train this property predictor? Thank you very much!

Successful environment setup

I've made several attempts at setting up the environment, and when I followed these steps, the code ran successfully.
I'm sharing this to help others who might encounter similar issues.

First of all, the content of the "env_copy.yml" file is as follows:

channels:
- pytorch
- conda-forge
- defaults
- pyg
dependencies:
- ase=3.22
- autopep8
- cudatoolkit=10.2
- jupyterlab
- matminer=0.7.3
- matplotlib
- nglview
- pip
- pyg=2.0.1
- ipywidgets
- pylint
- pymatgen==2023.8.10
- python=3.8.13
- pytorch-lightning=1.3.8
- pytorch=1.9.0
- seaborn
- tqdm
- pip:
  - hydra-core==1.1.0
  - hydra-joblib-launcher==1.1.5
  - p-tqdm==1.3.3
  - pytest
  - python-dotenv
  - smact==2.2.1
  - streamlit==0.79.0
  - wandb==0.10.33
name: cdvae_copy

Save the above content to the "env_copy.yml" file and then execute the following.

conda env create -f env_copy.yml
conda activate cdvae_copy

If you encounter the error

ImportError: cannot import name 'get_num_classes' from 'torchmetrics.utilities.data'

, then try the following steps:

pip uninstall torchmetrics==0.9.0.dev0
pip install torchmetrics==0.5

If you encounter the error

AttributionError: module 'distutils' has no attribute 'version'

, then try:
pip install setuptools==59.5.0

self-documenting strings (e.g. `f"{datasets=}, "` renders incompatibility with Python 3.7 (i.e. Google Colab)

Happy to PR this compatibility. Lmk if that sounds OK.

Property Models in cdvae/prop_models

I am trying to run compute_metrics on another dataset and ran into errors related to the models in the prop_models folder. Is there any information on how the models in the cdvae/prop_models folder were created?

Can you make the dependencies in env.yml more abstract and move the concrete dependencies to an env.lock.yml?

https://github.com/conda-incubator/conda-lock

Here's some additional info from an initialized PyScaffold template (probably only with the ds-project extension enabled):

Always keep your abstract (unpinned) dependencies updated in environment.yml and eventually
in setup.cfg if you want to ship and install your package via pip later on.
Create concrete dependencies as environment.lock.yml for the exact reproduction of your
environment with:
conda env export -n mp-time-split -f environment.lock.yml
For multi-OS development, consider using --no-builds during the export.
Update your current environment with respect to a new environment.lock.yml using:
conda env update -f environment.lock.yml --prune

Blurb above in commented out portion of mp-time-split and xtal2png README-s

Successful packages to run cdvae

Try to install these packages if want to run cdvae.
Package Version Editable project location

absl-py 1.4.0
aflow 0.0.11
aiohttp 3.8.5
aiosignal 1.3.1
altair 5.1.2
antlr4-python3-runtime 4.8
anyio 4.0.0
appdirs 1.4.4
APScheduler 3.10.4
argon2-cffi 23.1.0
argon2-cffi-bindings 21.2.0
arrow 1.3.0
ase 3.22.1
astor 0.8.1
astroid 3.0.0
asttokens 2.4.0
async-lru 2.0.4
async-timeout 4.0.2
attrs 23.1.0
autopep8 2.0.4
Babel 2.12.1
backcall 0.2.0
backports.functools-lru-cache 1.6.5
backports.zoneinfo 0.2.1
base58 2.1.1
beautifulsoup4 4.12.2
bleach 6.0.0
blinker 1.6.2
Brotli 1.1.0
cached-property 1.5.2
cachetools 5.3.1
cdvae 0.0.1 /home/abid/cdvae
certifi 2023.7.22
cffi 1.15.1
cftime 1.6.2
charset-normalizer 2.0.12
citrination-client 6.5.1
click 8.1.6
colorama 0.4.6
comm 0.1.4
configparser 6.0.0
contourpy 1.1.0
cryptography 41.0.4
cycler 0.11.0
debugpy 1.8.0
decorator 5.1.1
defusedxml 0.7.1
dill 0.3.7
dnspython 2.4.1
docker-pycreds 0.4.0
dpcpp-cpp-rt 2023.2.0
dscribe 2.1.0
entrypoints 0.4
exceptiongroup 1.1.3
executing 1.2.0
fair-research-login 0.3.1
fastjsonschema 2.18.1
filelock 3.9.0
Flask 3.0.0
fonttools 4.41.1
fqdn 1.5.1
frozenlist 1.4.0
fsspec 2023.6.0
future 0.18.3
gitdb 4.0.10
GitPython 3.1.32
globus-nexus-client 0.4.1
globus-sdk 3.28.0
gmpy2 2.1.2
google-auth 2.22.0
google-auth-oauthlib 1.0.0
googledrivedownloader 0.4
grpcio 1.56.2
h11 0.14.0
h2 4.1.0
h5py 3.9.0
hpack 4.0.0
httpcore 0.18.0
httplib2 0.22.0
hydra-core 1.1.0
hydra-joblib-launcher 1.1.5
hyperframe 6.0.1
idna 3.4
imageio 2.31.1
importlib-metadata 6.8.0
importlib-resources 6.0.0
iniconfig 2.0.0
intel-cmplr-lib-rt 2023.2.0
intel-cmplr-lic-rt 2023.2.0
intel-opencl-rt 2023.2.0
intel-openmp 2023.2.0
ipykernel 6.25.2
ipython 8.12.2
ipywidgets 8.1.1
isodate 0.6.1
isoduration 20.11.0
isort 5.12.0
itsdangerous 2.1.2
jedi 0.19.1
Jinja2 3.1.2
jmespath 1.0.1
joblib 1.1.0
json5 0.9.14
jsonpointer 2.4
jsonschema 4.18.4
jsonschema-specifications 2023.7.1
jupyter_client 8.3.1
jupyter_core 5.3.2
jupyter-events 0.7.0
jupyter-lsp 2.2.0
jupyter_server 2.7.3
jupyter_server_terminals 0.4.4
jupyterlab 4.0.6
jupyterlab-pygments 0.2.2
jupyterlab_server 2.25.0
jupyterlab-widgets 3.0.9
kiwisolver 1.4.4
latexcodec 2.0.1
lazy_loader 0.3
lightning-utilities 0.9.0
llvmlite 0.40.1
loguru 0.7.2
Markdown 3.4.4
MarkupSafe 2.1.1
matminer 0.7.3
matplotlib 3.7.2
matplotlib-inline 0.1.6
mccabe 0.7.0
mdf-forge 0.8.0
mdf-toolbox 0.5.8
mistune 3.0.1
mkl 2023.2.0
mkl-fft 1.3.6
mkl-random 1.2.2
mkl-service 2.4.0
monty 2023.5.8
mpmath 1.2.1
multidict 6.0.4
multiprocess 0.70.15
munkres 1.1.4
nbclient 0.8.0
nbconvert 7.8.0
nbformat 5.9.2
nest-asyncio 1.5.6
netCDF4 1.6.4
networkx 2.8.4
nglview 3.0.8
notebook_shim 0.2.3
numba 0.57.1
numpy 1.21.5
oauthlib 3.2.2
omegaconf 2.1.2
overrides 7.4.0
p-tqdm 1.3.3
packaging 21.3
palettable 3.3.3
pandas 2.0.3
pandocfilters 1.5.0
parso 0.8.3
pathos 0.3.1
pathtools 0.1.2
patsy 0.5.3
pexpect 4.8.0
pickleshare 0.7.5
Pillow 10.0.0
Pint 0.21.1
pip 23.2.1
pkgutil_resolve_name 1.3.10
platformdirs 3.11.0
plotly 5.15.0
pluggy 1.3.0
ply 3.11
plyfile 1.0.1
pooch 1.7.0
pox 0.3.3
ppft 1.7.6.7
prometheus-client 0.17.1
promise 2.3
prompt-toolkit 3.0.39
protobuf 3.20.1
psutil 5.9.0
ptyprocess 0.7.0
pure-eval 0.2.2
pyarrow 13.0.0
pyasn1 0.5.0
pyasn1-modules 0.3.0
pybind11 2.11.1
pybind11-global 2.11.1
pybtex 0.24.0
pycodestyle 2.11.0
pycparser 2.21
pydeck 0.8.1b0
pyDeprecate 0.3.0
Pygments 2.16.1
PyJWT 2.8.0
pylint 3.0.0
pymatgen 2022.4.26
pymongo 4.4.1
pyOpenSSL 23.2.0
pyparsing 3.0.9
pypif 2.1.1
PyQt5 5.15.9
PyQt5-sip 12.12.2
PySocks 1.7.1
pytest 7.4.2
python-dateutil 2.8.2
python-dotenv 1.0.0
python-json-logger 2.0.7
python-louvain 0.16
pytorch-lightning 1.3.8
pytz 2023.3
pyu2f 0.1.5
PyWavelets 1.4.1
PyYAML 5.4.1
pyzmq 25.1.1
rdflib 6.3.2
referencing 0.30.0
requests 2.31.0
requests-mock 1.11.0
requests-oauthlib 1.3.1
rfc3339-validator 0.1.4
rfc3986-validator 0.1.1
rpds-py 0.9.2
rsa 4.9
ruamel.yaml 0.17.32
ruamel.yaml.clib 0.2.7
scikit-learn 1.1.0
scipy 1.7.3
seaborn 0.13.0
Send2Trash 1.8.2
sentry-sdk 1.28.1
setproctitle 1.3.2
setuptools 59.5.0
shortuuid 1.0.11
sip 6.7.11
six 1.16.0
SMACT 2.2.1
smmap 5.0.0
sniffio 1.3.0
soupsieve 2.5
sparse 0.14.0
spglib 2.0.2
stack-data 0.6.2
statsmodels 0.14.0
streamlit 0.79.0
subprocess32 3.5.4
sympy 1.11.1
tabulate 0.9.0
tbb 2021.10.0
tenacity 8.2.2
tensorboard 2.14.1
tensorboard-data-server 0.7.1
termcolor 2.3.0
terminado 0.17.1
threadpoolctl 3.1.0
tifffile 2023.7.10
tinycss2 1.2.1
toml 0.10.2
tomli 2.0.1
tomlkit 0.12.1
toolz 0.12.0
torch 1.9.0+cu111
torch-cluster 1.5.9
torch-geometric 1.7.2
torch-scatter 2.0.9
torch-sparse 0.6.12
torch-spline-conv 1.2.1
torchmetrics 0.7.0
tornado 6.3.3
tqdm 4.65.0
traitlets 5.11.2
types-python-dateutil 2.8.19.14
typing_extensions 4.7.1
typing-utils 0.1.0
tzdata 2023.3
tzlocal 5.0.1
ujson 5.8.0
uncertainties 3.1.7
unicodedata2 15.1.0
uri-template 1.3.0
urllib3 1.26.11
validators 0.22.0
vtk 9.2.6
wandb 0.10.33
watchdog 3.0.0
wcwidth 0.2.8
webcolors 1.13
webencodings 0.5.1
websocket-client 1.6.3
Werkzeug 2.3.6
wheel 0.38.4
widgetsnbextension 4.0.9
wslink 1.12.2
yacs 0.1.8
yarl 1.9.2
zipp 3.16.2

Remember use the normal environment file that takes long time but eventually it works. You will face problem with version of torch, torch_sparse, torch_scatter, torch_cluster and torch_spline_conv. Try to install these mentioning their cuda compatibility. Like in my case it was cuda 11.1. As I installed torch 1.9.0 with cuda 11.1.

What code was used to visualize the structures?

It looks a lot like pymatviz (see struct_vis), but I didn't think that pymatviz dealt with transparency, so I'm assuming it's something else.

https://github.com/txie-93/cdvae/tree/main/data/perov_5#visualization-of-structures

Any built-in option to run on a dummy dataset? (e.g. 50 compounds instead of 10k)

To run the code quickly while testing

Why is my training process not complete？

while parsing CIF: Some fractional coordinates rounded to ideal values to avoid issues with finite precision.
  warnings.warn("Issues encountered while parsing CIF: " + "\n".join(self.warnings))
100%|█████████▉| 11351/11356 [09:20<00:00, 26.38it/s]

Evaluation metrics for the generation task

Sorry for bothering you with all these questions ...

As you know I didn't manage to execute the reconstruction task, but I think the generation task finished succefully. The program created a file called eval_gen.pt

However when I run:

python scripts/compute_metrics.py --root_path /home/CDVAE/cdvae-main/hydra/singlerun/2022-02-10/perov/ --tasks gen

I get the error:

FileNotFoundError: [Errno 2] No such file or directory: '/home/CDVAE/cdvae-main/hydra/singlerun/2022-02-10/perov/eval_recon.pt'

Does this mean the evaluation of the generation task is dependent on results of the reconstruction task ?

Luis

Problem with the dataset MP-20

I found another issue... Maybe this is also related to changes in pytorch?

I think I managed to train the CDVAE with the datasets Perov-5 and Carbon-24, but when I try to train it with the dataset MP-20, I get the following error:

Epoch 46: 0%| | 0/212 [00:00<?, ?it/s, loss=2.08e+28, v_num=0sev, val_loss=11.Error executing job with overrides: ['data=mp_20', 'expname=mp_20']
Traceback (most recent call last):
File "cdvae/run.py", line 166, in main
run(cfg)
File "cdvae/run.py", line 154, in run
trainer.fit(model=model, datamodule=datamodule)
File "/home/.conda/envs/cdvae2/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 460, in fit
self._run(model)
File "/home/.conda/envs/cdvae2/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 758, in _run
self.dispatch()
File "/home/.conda/envs/cdvae2/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 799, in dispatch
self.accelerator.start_training(self)
File "/home/.conda/envs/cdvae2/lib/python3.8/site-packages/pytorch_lightning/accelerators/accelerator.py", line 96, in start_training
self.training_type_plugin.start_training(trainer)
File "/home/.conda/envs/cdvae2/lib/python3.8/site-packages/pytorch_lightning/plugins/training_type/training_type_plugin.py", line 144, in start_training
self._results = trainer.run_stage()
File "/home/.conda/envs/cdvae2/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 809, in run_stage
return self.run_train()
File "/home/.conda/envs/cdvae2/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 871, in run_train
self.train_loop.run_training_epoch()
File "/home/.conda/envs/cdvae2/lib/python3.8/site-packages/pytorch_lightning/trainer/training_loop.py", line 499, in run_training_epoch
batch_output = self.run_training_batch(batch, batch_idx, dataloader_idx)
File "/home/.conda/envs/cdvae2/lib/python3.8/site-packages/pytorch_lightning/trainer/training_loop.py", line 738, in run_training_batch
self.optimizer_step(optimizer, opt_idx, batch_idx, train_step_and_backward_closure)
File "/home/.conda/envs/cdvae2/lib/python3.8/site-packages/pytorch_lightning/trainer/training_loop.py", line 434, in optimizer_step
model_ref.optimizer_step(
File "/home/.conda/envs/cdvae2/lib/python3.8/site-packages/pytorch_lightning/core/lightning.py", line 1403, in optimizer_step
optimizer.step(closure=optimizer_closure)
File "/home/.conda/envs/cdvae2/lib/python3.8/site-packages/pytorch_lightning/core/optimizer.py", line 214, in step
self.__optimizer_step(*args, closure=closure, profiler_name=profiler_name, **kwargs)
File "/home/.conda/envs/cdvae2/lib/python3.8/site-packages/pytorch_lightning/core/optimizer.py", line 134, in __optimizer_step
trainer.accelerator.optimizer_step(optimizer, self._optimizer_idx, lambda_closure=closure, **kwargs)
File "/home/.conda/envs/cdvae2/lib/python3.8/site-packages/pytorch_lightning/accelerators/accelerator.py", line 329, in optimizer_step
self.run_optimizer_step(optimizer, opt_idx, lambda_closure, **kwargs)
File "/home/.conda/envs/cdvae2/lib/python3.8/site-packages/pytorch_lightning/accelerators/accelerator.py", line 336, in run_optimizer_step
self.training_type_plugin.optimizer_step(optimizer, lambda_closure=lambda_closure, **kwargs)
File "/home/.conda/envs/cdvae2/lib/python3.8/site-packages/pytorch_lightning/plugins/training_type/training_type_plugin.py", line 193, in optimizer_step
optimizer.step(closure=lambda_closure, **kwargs)
File "/home/.conda/envs/cdvae2/lib/python3.8/site-packages/torch/optim/optimizer.py", line 89, in wrapper
return func(*args, **kwargs)
File "/home/.conda/envs/cdvae2/lib/python3.8/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
return func(*args, **kwargs)
File "/home/.conda/envs/cdvae2/lib/python3.8/site-packages/torch/optim/adam.py", line 66, in step
loss = closure()
File "/home/.conda/envs/cdvae2/lib/python3.8/site-packages/pytorch_lightning/trainer/training_loop.py", line 732, in train_step_and_backward_closure
result = self.training_step_and_backward(
File "/home/.conda/envs/cdvae2/lib/python3.8/site-packages/pytorch_lightning/trainer/training_loop.py", line 823, in training_step_and_backward
result = self.training_step(split_batch, batch_idx, opt_idx, hiddens)
File "/home/.conda/envs/cdvae2/lib/python3.8/site-packages/pytorch_lightning/trainer/training_loop.py", line 290, in training_step
training_step_output = self.trainer.accelerator.training_step(args)
File "/home/.conda/envs/cdvae2/lib/python3.8/site-packages/pytorch_lightning/accelerators/accelerator.py", line 204, in training_step
return self.training_type_plugin.training_step(*args)
File "/home/.conda/envs/cdvae2/lib/python3.8/site-packages/pytorch_lightning/plugins/training_type/training_type_plugin.py", line 155, in training_step
return self.lightning_module.training_step(*args, **kwargs)
File "/home/CDVAE/cdvae-main/cdvae/pl_modules/model.py", line 528, in training_step
outputs = self(batch, teacher_forcing, training=True)
File "/home/.conda/envs/cdvae2/lib/python3.8/site-packages/torch/nn/modules/module.py", line 889, in _call_impl
result = self.forward(*input, **kwargs)
File "/home/CDVAE/cdvae-main/cdvae/pl_modules/model.py", line 338, in forward
rand_atom_types = torch.multinomial(
RuntimeError: probability tensor contains either inf, nan or element < 0

InstantiationException: a leaf Variable that requires grad is being used in an in-place operation

Followed the faster conda installation instructions from the README on WSL2 (files and environment localized to the WSL2 storage) and got the same error as in #2 (comment).

Exception has occurred: InstantiationException       (note: full exception trace is shown but execution is paused at: _run_module_as_main)
Error in call to target 'cdvae.pl_modules.model.CDVAE':
InstantiationException("Error in call to target 'cdvae.pl_modules.gnn.DimeNetPlusPlusWrap':\nRuntimeError('a leaf Variable that requires grad is being used in an in-place operation.')\nfull_key: encoder")
full_key: model
  File "/home/sgbaird/miniconda3/envs/cdvae/lib/python3.9/site-packages/hydra/_internal/instantiate/_instantiate2.py", line 92, in _call_target
    return _target_(*args, **kwargs)
  File "/home/sgbaird/cdvae/cdvae/pl_modules/gnn.py", line 327, in __init__
    super(DimeNetPlusPlusWrap, self).__init__(
  File "/home/sgbaird/cdvae/cdvae/pl_modules/gnn.py", line 223, in __init__
    self.rbf = BesselBasisLayer(num_radial, cutoff, envelope_exponent)
  File "/home/sgbaird/miniconda3/envs/cdvae/lib/python3.9/site-packages/torch_geometric/nn/models/dimenet.py", line 59, in __init__
    self.reset_parameters()
  File "/home/sgbaird/miniconda3/envs/cdvae/lib/python3.9/site-packages/torch_geometric/nn/models/dimenet.py", line 62, in reset_parameters
    torch.arange(1, self.freq.numel() + 1, out=self.freq).mul_(PI)

The above exception was the direct cause of the following exception:

  File "/home/sgbaird/miniconda3/envs/cdvae/lib/python3.9/site-packages/hydra/_internal/instantiate/_instantiate2.py", line 92, in _call_target
    return _target_(*args, **kwargs)
  File "/home/sgbaird/cdvae/cdvae/pl_modules/model.py", line 140, in __init__
    self.encoder = hydra.utils.instantiate(
  File "/home/sgbaird/miniconda3/envs/cdvae/lib/python3.9/site-packages/hydra/_internal/instantiate/_instantiate2.py", line 222, in instantiate
    return instantiate_node(
  File "/home/sgbaird/miniconda3/envs/cdvae/lib/python3.9/site-packages/hydra/_internal/instantiate/_instantiate2.py", line 339, in instantiate_node
    return _call_target(_target_, partial, args, kwargs, full_key)
  File "/home/sgbaird/miniconda3/envs/cdvae/lib/python3.9/site-packages/hydra/_internal/instantiate/_instantiate2.py", line 97, in _call_target
    raise InstantiationException(msg) from e

The above exception was the direct cause of the following exception:

  File "/home/sgbaird/miniconda3/envs/cdvae/lib/python3.9/site-packages/hydra/_internal/instantiate/_instantiate2.py", line 97, in _call_target
    raise InstantiationException(msg) from e
  File "/home/sgbaird/miniconda3/envs/cdvae/lib/python3.9/site-packages/hydra/_internal/instantiate/_instantiate2.py", line 339, in instantiate_node
    return _call_target(_target_, partial, args, kwargs, full_key)
  File "/home/sgbaird/miniconda3/envs/cdvae/lib/python3.9/site-packages/hydra/_internal/instantiate/_instantiate2.py", line 222, in instantiate
    return instantiate_node(
  File "/home/sgbaird/cdvae/cdvae/run.py", line 94, in run
    model: pl.LightningModule = hydra.utils.instantiate(
  File "/home/sgbaird/cdvae/cdvae/run.py", line 166, in main
    run(cfg)
  File "/home/sgbaird/miniconda3/envs/cdvae/lib/python3.9/site-packages/hydra/core/utils.py", line 186, in run_job
    ret.return_value = task_function(task_cfg)
  File "/home/sgbaird/miniconda3/envs/cdvae/lib/python3.9/site-packages/hydra/core/utils.py", line 260, in return_value
    raise self._return_value
  File "/home/sgbaird/miniconda3/envs/cdvae/lib/python3.9/site-packages/hydra/_internal/hydra.py", line 132, in run
    _ = ret.return_value
  File "/home/sgbaird/miniconda3/envs/cdvae/lib/python3.9/site-packages/hydra/_internal/utils.py", line 453, in <lambda>
    lambda: hydra.run(
  File "/home/sgbaird/miniconda3/envs/cdvae/lib/python3.9/site-packages/hydra/_internal/utils.py", line 216, in run_and_report
    raise ex
  File "/home/sgbaird/miniconda3/envs/cdvae/lib/python3.9/site-packages/hydra/_internal/utils.py", line 216, in run_and_report
    raise ex
  File "/home/sgbaird/miniconda3/envs/cdvae/lib/python3.9/site-packages/hydra/_internal/utils.py", line 452, in _run_app
    run_and_report(
  File "/home/sgbaird/miniconda3/envs/cdvae/lib/python3.9/site-packages/hydra/_internal/utils.py", line 389, in _run_hydra
    _run_app(
  File "/home/sgbaird/miniconda3/envs/cdvae/lib/python3.9/site-packages/hydra/main.py", line 90, in decorated_main
    _run_hydra(
  File "/home/sgbaird/cdvae/cdvae/run.py", line 170, in <module>
    main()
  File "/home/sgbaird/miniconda3/envs/cdvae/lib/python3.9/runpy.py", line 87, in _run_code
    exec(code, run_globals)
  File "/home/sgbaird/miniconda3/envs/cdvae/lib/python3.9/runpy.py", line 97, in _run_module_code
    _run_code(code, mod_globals, init_globals,
  File "/home/sgbaird/miniconda3/envs/cdvae/lib/python3.9/runpy.py", line 268, in run_path
    return _run_module_code(code, init_globals, run_name,
  File "/home/sgbaird/miniconda3/envs/cdvae/lib/python3.9/runpy.py", line 87, in _run_code
    exec(code, run_globals)
  File "/home/sgbaird/miniconda3/envs/cdvae/lib/python3.9/runpy.py", line 197, in _run_module_as_main (Current frame)
    return _run_code(code, main_globals, None,

Unable to run CDVAE or training script

On multiple systems (GPU and CPU), I've tried:

Cloning the repository
Creating the conda environment (both with the cpu-only yaml and the gpu yaml)
Changing .env.template to .env and editing the directories listed in it
Trying to run the training script with python cdvae/run.py data=perov expname=perov.

No matter how I try to configure my system, I always get this error upon trying to run the training process:

This means that I'm not able to train or use CDVAE at all. Can someone explain this dependency issue, and how to get around it? Thank you so much!

Was elemental filtering applied to the starting datasets?

E.g. don't include noble gases, don't include radioactive elements. Went through the manuscript and had trouble finding if/where this was clarified.

hydra.errors.InstantiationException: Error locating target 'cdvae.pl_data.datamodule.CrystDataModule', set env var HYDRA_FULL_ERROR=1 to see chained exception. full_key: data.datamodule

hi~,how to solve this error

import error

When I try to run the code, it seems to be an import error in "from torch_geometric.nn.acts import swish
". I can't find the acts under torch_geometric.nn
I also tried to import SiLU instead of swish but still didn't work

Running `mp-20` is making my Dell-G7 on Windows non-responsive, can't run basic usage

I end up getting some combination of my computer becoming non-responsive/needing to force quit VS Code and the following error message:

[WinError 1455] The paging file is too small for this operation to complete. Error loading "C:\Users\sterg\Miniconda3\envs\cdvae\lib\site-packages\torch\lib\caffe2_detectron_ops_gpu.dll" or one of its dependencies.
  File "C:\Users\sterg\miniconda3\envs\cdvae\Lib\site-packages\torch\__init__.py", line 123, in <module>
    raise err
  File "C:\Users\sterg\Documents\GitHub\sparks-baird\cdvae\cdvae\run.py", line 6, in <module>
    import torch
  File "C:\Users\sterg\miniconda3\envs\cdvae\Lib\runpy.py", line 87, in _run_code
    exec(code, run_globals)
  File "C:\Users\sterg\miniconda3\envs\cdvae\Lib\runpy.py", line 97, in _run_module_code
    _run_code(code, mod_globals, init_globals,
  File "C:\Users\sterg\miniconda3\envs\cdvae\Lib\runpy.py", line 265, in run_path
    return _run_module_code(code, init_globals, run_name,
  File "C:\Users\sterg\miniconda3\envs\cdvae\Lib\site-packages\multiprocess\spawn.py", line 287, in _fixup_main_from_path
    main_content = runpy.run_path(main_path,
  File "C:\Users\sterg\miniconda3\envs\cdvae\Lib\site-packages\multiprocess\spawn.py", line 236, in prepare
    _fixup_main_from_path(data['init_main_from_path'])
  File "C:\Users\sterg\miniconda3\envs\cdvae\Lib\site-packages\multiprocess\spawn.py", line 125, in _main
    prepare(preparation_data)
  File "C:\Users\sterg\miniconda3\envs\cdvae\Lib\site-packages\multiprocess\spawn.py", line 116, in spawn_main
    exitcode = _main(fd, parent_sentinel)
  File "<string>", line 1, in <module> (Current frame)

Which discusses a somewhat involved / hacky workaround described in https://stackoverflow.com/a/69489193/13697228 and that this will probably no longer be an issue with Cuda 11.7.

Incidentally, despite running PyTorch for a bunch of different projects, this is the first time I'm seeing this one.

Error while running the code

When running to code for the example provided, have a syntax error

File "", line 1
(cfg.train.pl_trainer.fast_dev_run=)

SyntaxError: invalid syntax

How do I fix this?

Question about the reconstruction task

Hi authors,

I have a question about the reconstruction experiments in CDVAE. I have tried running the training on mp_20 dataset, and run these commands to do the reconstruction and evaluation following instructions in README.md:

python scripts/evaluate.py --model_path /home/lyz/cdvae/hydra/singlerun/2022-04-04/mp_20/ --tasks recon
python scripts/compute_metrics.py --root_path /home/lyz/cdvae/hydra/singlerun/2022-04-04/mp_20/ --tasks recon

However, the reconstruction match rate is very low:

{"match_rate": 0.16604023877957108, "rms_dist": 0.05085572933343641}

I noticed that there is an option to only reconstruct coordinates and lattices while keeping ground truth atom types and numbers, and tried this option as:

python scripts/evaluate.py --model_path /home/lyz/cdvae/hydra/singlerun/2022-04-04/mp_20/ --tasks recon --force_num_atoms --force_atom_types

This time the reconstruction match rate is much higher:

{"match_rate": 0.3720981649347778, "rms_dist": 0.14127310664427778}

Hence I think whether two materials match is related to their atom types. I am wondering for the reconstruction performance result reported in Table 1 of the paper, do you reconstruct all things about materials in the test set, or only reconstruct coordinates and lattices and keeping ground truth atom types and numbers?

Thank you for your possible help!

Best

visualization problem

I would like to inquire how I can obtain the visualization results in the published paper.

Clarification on normalizing the lattice parameter lengths

We normalize the lengths of lattice vectors with [N^(1/3)]

Does this mean: $a_{scaled} = \frac{a}{N^{1/3}}$ ?

A problem while training model without a property predictor

Error executing job with overrides: ['data=mp_20', 'expname=mp_20']
Traceback (most recent call last):
File "/home/scms/lixiaoyi/anaconda3/envs/cdvae/lib/python3.8/site-packages/hydra/_internal/utils.py", line 575, in _locate
import_module(mod)
File "/home/scms/lixiaoyi/anaconda3/envs/cdvae/lib/python3.8/importlib/init.py", line 127, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "", line 1014, in _gcd_import
File "", line 991, in _find_and_load
File "", line 975, in _find_and_load_unlocked
File "", line 671, in _load_unlocked
File "", line 843, in exec_module
File "", line 219, in _call_with_frames_removed
File "/gpfs/home/scms/lixiaoyi/cdvae/cdvae/pl_data/datamodule.py", line 15, in
from cdvae.common.data_utils import get_scaler_from_data_list
File "/gpfs/home/scms/lixiaoyi/cdvae/cdvae/common/data_utils.py", line 10, in
from pymatgen.analysis.graphs import StructureGraph
File "/home/scms/lixiaoyi/anaconda3/envs/cdvae/lib/python3.8/site-packages/pymatgen/analysis/graphs.py", line 22, in
from monty.os.path import which
ImportError: cannot import name 'which' from 'monty.os.path' (/home/scms/lixiaoyi/anaconda3/envs/cdvae/lib/python3.8/site-packages/monty/os/path.py)

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
File "/gpfs/home/scms/lixiaoyi/cdvae/cdvae/run.py", line 166, in main
run(cfg)
File "/gpfs/home/scms/lixiaoyi/cdvae/cdvae/run.py", line 88, in run
datamodule: pl.LightningDataModule = hydra.utils.instantiate(
File "/home/scms/lixiaoyi/anaconda3/envs/cdvae/lib/python3.8/site-packages/hydra/_internal/instantiate/_instantiate2.py", line 180, in instantiate
return instantiate_node(config, *args, recursive=recursive, convert=convert)
File "/home/scms/lixiaoyi/anaconda3/envs/cdvae/lib/python3.8/site-packages/hydra/_internal/instantiate/_instantiate2.py", line 240, in instantiate_node
target = _resolve_target(node.get(_Keys.TARGET))
File "/home/scms/lixiaoyi/anaconda3/envs/cdvae/lib/python3.8/site-packages/hydra/_internal/instantiate/_instantiate2.py", line 104, in _resolve_target
return _locate(target)
File "/home/scms/lixiaoyi/anaconda3/envs/cdvae/lib/python3.8/site-packages/hydra/_internal/utils.py", line 577, in _locate
raise ImportError(
ImportError: Encountered error: cannot import name 'which' from 'monty.os.path' (/home/scms/lixiaoyi/anaconda3/envs/cdvae/lib/python3.8/site-packages/monty/os/path.py) when loading module 'cdvae.pl_data.datamodule.CrystDataModule'

Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.

RuntimeError: General Union types are not currently supported.

Need Solution!

fn = torch._C._jit_script_compile(
qualified_name, ast, _rcb, get_default_args(obj)
)
RuntimeError:
General Union types are not currently supported. Only Union[T, NoneType] (i.e. Optional[T]) is supported.:
/lib/python3.8/site-packages/torch_cluster/rw.py", line 18
num_nodes: Optional[int] = None,
return_edge_indices: bool = False,
) -> Union[Tensor, Tuple[Tensor, Tensor]]:
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE
"""Samples random walks of length :obj:walk_length from all node indices
in :obj:start in the graph given by :obj:(row, col) as described in the
` when loading module 'cdvae.pl_modules.gnn.DimeNetPlusPlusWrap'

Decoder loss

Hi,

Very interesting work! A question, though: in the paper equation 2 (the decoder loss) shouldn’t the score, s, be divided by sigma (i.e., so that it becomes the score conditioned on sigma), and d_min be divided by sigma^2 (instead of just sigma)? This is what I get from comparing the work by Song and Ermon, and it also seems to be how you have implemented it here in the codebase. Or maybe I have misunderstood something?

Unable to create new environment from env.cpu.yml file

This is what outputs in Anaconda Prompt:

`(base) C:\Users\colto\Desktop>conda env create -f env.cpu.yml

Collecting package metadata (repodata.json): done

Solving environment: /

Found conflicts! Looking for incompatible packages.

UnsatisfiableError: The following specifications were found to be incompatible with each other:

Output in format: Requested package -> Available versions

Package requests conflicts for:
pyg=2.0.1 -> requests
python=3.8 -> pip -> requests
matminer=0.7.3 -> citrination-client[version='>=4.0.1'] -> requests[version='>=2.18.4']
jupyterlab -> jupyterlab_server[version='>=2.10,<3'] -> requests
matminer=0.7.3 -> requests[version='>=2.20.0']
pytorch-lightning=1.3.8 -> tensorboard[version='>=2.2.0,!=2.5.0'] -> requests[version='>=2.21.0|>=2.21.0,<3']
pymatgen=2020.12.31 -> requests
pip -> requests

Package zlib conflicts for:
matplotlib -> zlib[version='>=1.2.11,<1.3.0a0']
jupyterlab -> python[version='>=3.7'] -> zlib[version='>=1.2.11,<1.3.0a0']
matminer=0.7.3 -> python[version='>=3.5'] -> zlib[version='>=1.2.11,<1.3.0a0']
pymatgen=2020.12.31 -> matplotlib-base[version='>=1.5'] -> zlib[version='1.2.|1.2.11.|>=1.2.11,<1.3.0a0|>=1.2.12,<1.3.0a0']
python=3.8 -> pypy3.8=7.3.9 -> zlib[version='>=1.2.11,<1.3.0a0']
pytorch-lightning=1.3.8 -> python[version='>=3.6'] -> zlib[version='>=1.2.11,<1.3.0a0']
pylint -> python[version='>=3.7.2'] -> zlib[version='>=1.2.11,<1.3.0a0']
seaborn -> matplotlib-base[version='>=2.1.2'] -> zlib[version='>=1.2.11,<1.3.0a0']
pip -> python[version='>=3.7'] -> zlib[version='>=1.2.11,<1.3.0a0']
matplotlib -> freetype=2.6 -> zlib[version='1.2.*|1.2.11|1.2.8']
autopep8 -> python[version='>=3.6'] -> zlib[version='>=1.2.11,<1.3.0a0']
ase=3.22 -> matplotlib-base -> zlib[version='>=1.2.11,<1.3.0a0']
ipywidgets -> python[version='>=3.3'] -> zlib[version='>=1.2.11,<1.3.0a0']
tqdm -> python[version='>=2.7'] -> zlib[version='>=1.2.11,<1.3.0a0']
nglview -> python[version='>=3.6'] -> zlib[version='>=1.2.11,<1.3.0a0']

Package pypy3.7 conflicts for:
tqdm -> python[version='>=2.7'] -> pypy3.7[version='7.3.5.|7.3.7.']
seaborn -> statsmodels[version='>=0.8.0'] -> pypy3.7[version='7.3.5.|7.3.7.|>=7.3.5|>=7.3.7']
matplotlib -> python[version='>=3.7,<3.8.0a0'] -> pypy3.7[version='7.3.|7.3.5.|7.3.7.']
pytorch-lightning=1.3.8 -> future -> pypy3.7[version='7.3.5.|7.3.7.|>=7.3.5|>=7.3.7']
matminer=0.7.3 -> future[version='>=0.16.0'] -> pypy3.7[version='7.3.5.|7.3.7.|>=7.3.5|>=7.3.7']
pyg=2.0.1 -> numpy -> pypy3.7[version='7.3.5.|7.3.7.|>=7.3.5|>=7.3.7']
jupyterlab -> ipython -> pypy3.7[version='7.3.5.|7.3.7.|>=7.3.5|>=7.3.7']
ase=3.22 -> matplotlib-base -> pypy3.7[version='7.3.5.|7.3.7.|>=7.3.5|>=7.3.7']
nglview -> numpy -> pypy3.7[version='7.3.5.|7.3.7.|>=7.3.5|>=7.3.7']
pymatgen=2020.12.31 -> apscheduler[version='>=2.1.0'] -> pypy3.7[version='7.3.5.|7.3.7.|>=7.3.5|>=7.3.7']
autopep8 -> python[version='>=3.6'] -> pypy3.7[version='7.3.5.|7.3.7.']
pytorch=1.8.1 -> numpy[version='>=1.19'] -> pypy3.7[version='7.3.5.|7.3.7.|>=7.3.5|>=7.3.7']
matplotlib -> pypy3.7[version='>=7.3.5|>=7.3.7']
pylint -> python[version='>=3.7.2'] -> pypy3.7[version='7.3.5.|7.3.7.']
ipywidgets -> ipython[version='>=4.0.0'] -> pypy3.7[version='7.3.5.|7.3.7.|>=7.3.5|>=7.3.7']
pip -> python[version='>=3.7'] -> pypy3.7[version='7.3.5.|7.3.7.*|>=7.3.7|>=7.3.5']

Package setuptools conflicts for:
pymatgen=2020.12.31 -> apscheduler[version='>=2.1.0'] -> setuptools[version='<60.0.0']
autopep8 -> pycodestyle[version='>=2.3'] -> setuptools
python=3.8 -> pip -> setuptools
jupyterlab -> ipython -> setuptools[version='>=18.5|>=46.4.0|>=60.2.0']
pylint -> astroid[version='>=2.11.6,<2.12.0'] -> setuptools[version='>=20.0']
ase=3.22 -> matplotlib-base -> setuptools
seaborn -> matplotlib-base[version='>=2.1.2'] -> setuptools[version='<60.0.0']
pytorch-lightning=1.3.8 -> tensorboard[version='>=2.2.0,!=2.5.0'] -> setuptools[version='>=41.0.0|>=41.4']
matplotlib -> setuptools
matminer=0.7.3 -> citrination-client[version='>=4.0.1'] -> setuptools[version='<60.0.0']
nglview -> ipykernel -> setuptools[version='>=60']
ipywidgets -> ipykernel[version='>=4.5.1'] -> setuptools[version='>=18.5|>=60']
pip -> setuptools
pyg=2.0.1 -> jinja2 -> setuptools[version='<60.0.0']

Package vs2010_runtime conflicts for:
ipywidgets -> python[version='>=3.3'] -> vs2010_runtime
tqdm -> python[version='>=2.7'] -> vs2010_runtime
pylint -> python=3.4 -> vs2010_runtime
seaborn -> python -> vs2010_runtime
jupyterlab -> python=3.4 -> vs2010_runtime
autopep8 -> python -> vs2010_runtime
matplotlib -> python=3.4 -> vs2010_runtime
nglview -> python[version='>=3'] -> vs2010_runtime
pip -> python[version='>=3'] -> vs2010_runtime

Package python conflicts for:
ipywidgets -> python[version='2.7.|3.5.|3.6.|>=2.7,<2.8.0a0|>=3.3|>=3.6,<3.7.0a0|>=3.5,<3.6.0a0|3.4.|>=3.7,<3.8.0a0']
ase=3.22 -> flask -> python[version='2.7.|3.5.|3.6.|>=2.7,<2.8.0a0|>=3.6,<3.7.0a0|>=3.6|>=3.7|>=3.5,<3.6.0a0|3.4.|>=3.7,<3.8.0a0|>=3.10,<3.11.0a0|>=3.9,<3.10.0a0|>=3.8,<3.9.0a0']
jupyterlab -> ipython -> python[version='>=2.7|>=3.10,<3.11.0a0|>=3.9,<3.10.0a0|>=3.8,<3.9.0a0|>=3']
nglview -> python[version='2.7.|3.5.|3.6.|>=3|>=3.6']
ipywidgets -> ipykernel[version='>=4.5.1'] -> python[version='>=3.10,<3.11.0a0|>=3.7|>=3.9,<3.10.0a0|>=3.8,<3.9.0a0|>=3.6|>=3.5']
matminer=0.7.3 -> python[version='>=3.5']
pip -> python[version='2.7.|3.5.|3.6.|>=2.7,<2.8.0a0|>=3.7,<3.8.0a0|>=3.8,<3.9.0a0|>=3|>=3.6|>=3.7|>=3.6,<3.7.0a0|>=3.5,<3.6.0a0|3.4.|>=3.9,<3.10.0a0|>=3.10,<3.11.0a0']
ase=3.22 -> python[version='>=3.5']
pip -> wheel -> python[version='!=3.0,!=3.1,!=3.2,!=3.3,!=3.4|2.7.|>=3.6|2.7|>=3.6|>=2.7|>=3.6,<4.0']
tqdm -> colorama -> python[version='>=3.6']
seaborn -> python[version='2.7.|3.5.|3.6.|>=3.6|3.4.|>=3.7,<3.8.0a0|>=2.7,<2.8.0a0|>=3.5,<3.6.0a0|>=3.6,<3.7.0a0']
pytorch-lightning=1.3.8 -> fsspec[version='>=2021.4.0'] -> python[version='2.7.|3.4.|3.5.|3.6.|>=2.7,<2.8.0a0|>=3.10,<3.11.0a0|>=3.8,<3.9.0a0|>=3.9,<3.10.0a0|>=3.7,<3.8.0a0|>=3.7|>=3.6,<3.7.0a0|>=3.5,<3.6.0a0|>=2.7|>=3.2|>=3.2,<3.10|>=3.5']
pytorch=1.8.1 -> python[version='>=3.6,<3.7.0a0|>=3.9,<3.10.0a0|>=3.8,<3.9.0a0|>=3.7,<3.8.0a0']
matminer=0.7.3 -> aflow[version='>=0.0.9'] -> python[version='2.7.|3.5.|3.6.|>=3.9,<3.10.0a0|>=3.10,<3.11.0a0|>=3.8,<3.9.0a0|>=3.7,<3.8.0a0|>=3.6,<3.7.0a0|>=2.7,<2.8.0a0|>=3.5,<3.6.0a0|3.4.|>=3|>=3.7|>=3.6|>=3.7.1,<3.8.0a0|>=3.8|>=3.6,<4.0|>=2.7']
seaborn -> statsmodels[version='>=0.8.0'] -> python[version='>=3.10,<3.11.0a0|>=3.8,<3.9.0a0|>=3.9,<3.10.0a0|>=3.7.1,<3.8.0a0']
jupyterlab -> python[version='2.7.|3.5.|3.6.|>=2.7,<2.8.0a0|>=3.5,<3.6.0a0|>=3.5|>=3.6|>=3.7|>=3.7,<3.8.0a0|>=3.6,<3.7.0a0|3.4.']
pylint -> colorama -> python[version='3.7.|>=2.7|>=3.5|>=3.6|>=3.6,<4.0|>=3.6.1,<4.0|>=3.7|3.8.|3.9.']
autopep8 -> pycodestyle[version='>=2.8'] -> python[version='2.7.|>=3.5|>=2.7|>=3.8,<3.9.0a0|>=3.10,<3.11.0a0|>=3.9,<3.10.0a0']
pylint -> python[version='2.7.|3.5.|3.6.|>=2.7,<2.8.0a0|>=3.5,<3.6.0a0|>=3.6,<3.7.0a0|>=3.6,<4|>=3.6.2,<4|>=3.6.2|>=3.7.2|>=3.7,<3.8.0a0|>=3.8,<3.9.0a0|>=3.9,<3.10.0a0|3.4.|>=3.10,<3.11.0a0']
tqdm -> python[version='2.7.|3.5.|3.6.|>=2.7|>=2.7,<2.8.0a0|>=3.8,<3.9.0a0|>=3.7,<3.8.0a0|>=3.6,<3.7.0a0|3.4.|>=3.9,<3.10.0a0|>=3.10,<3.11.0a0|>=3.5,<3.6.0a0']
matplotlib -> python[version='3.4.|3.5.|>=2.7,<2.8.0a0|>=3.10,<3.11.0a0|>=3.7,<3.8.0a0|>=3.9,<3.10.0a0|>=3.8,<3.9.0a0|>=3.6,<3.7.0a0|>=3.5,<3.6.0a0']
pymatgen=2020.12.31 -> apscheduler[version='>=2.1.0'] -> python[version='2.7.|3.5.|3.6.|>=2.7,<2.8.0a0|>=3.10,<3.11.0a0|>=3.5,<3.6.0a0|3.4.|>=3.5|>=3.6|>=3.8|>=3.7|>=3.7.1,<3.8.0a0|>=3|3.7.|>=3.6,<4.0|2.7.|>=3.5|>=2.7|>=3.6,<3.7|3.9.|3.8.']
nglview -> ipywidgets[version='>=7'] -> python[version='3.4.|>=2.7,<2.8.0a0|>=3.3|>=3.6,<3.7.0a0|>=3.5,<3.6.0a0|>=3.7,<3.8.0a0|>=3.9,<3.10.0a0|>=3.8,<3.9.0a0|>=3.10,<3.11.0a0|>=3.7']
autopep8 -> python[version='2.7.|3.5.|3.6.|>=3.6|3.4.|>=3.6,<3.7.0a0|>=3.7,<3.8.0a0|>=2.7,<2.8.0a0|>=3.5,<3.6.0a0']
python=3.8
matplotlib -> pyqt -> python[version='2.7.|3.6.|3.10.|3.7.|3.9.|3.8.*|>=3.6|>=3|<3']
pytorch-lightning=1.3.8 -> python[version='>=3.6']
pytorch=1.8.1 -> ninja -> python[version='>=2.7,<2.8.0a0|>=3.10,<3.11.0a0|>=3.5,<3.6.0a0|>=3.7|>=3.6|>=3.5|>=3.6,<3.7']
pymatgen=2020.12.31 -> python[version='>=3.6,<3.7.0a0|>=3.7,<3.8.0a0|>=3.9,<3.10.0a0|>=3.8,<3.9.0a0']

Package icc_rt conflicts for:
pytorch-lightning=1.3.8 -> numpy[version='>=1.17.2'] -> icc_rt[version='>=2019.0.0']
seaborn -> numpy[version='>=1.13.3'] -> icc_rt[version='>=13.1.6|>=2019.0.0|>=16.0.4']
matminer=0.7.3 -> numpy[version='>=1.18.3'] -> icc_rt[version='>=2019.0.0']
pyg=2.0.1 -> numpy -> icc_rt[version='>=13.1.6|>=2019.0.0|>=16.0.4']
ase=3.22 -> numpy -> icc_rt[version='>=13.1.6|>=2019.0.0|>=16.0.4']
matplotlib -> numpy=1.11 -> icc_rt[version='>=13.1.6|>=2019.0.0|>=16.0.4']
pymatgen=2020.12.31 -> scipy[version='>=1.4.1'] -> icc_rt[version='>=2019.0.0']
nglview -> numpy -> icc_rt[version='>=13.1.6|>=2019.0.0|>=16.0.4']

Package charset-normalizer conflicts for:
pymatgen=2020.12.31 -> requests -> charset-normalizer[version='>=2,<3|>=2.0.0,<2.1|>=2.0.0,<2.0.1|>=2.0.0,<2.1.0']
pip -> requests -> charset-normalizer[version='>=2,<3|>=2.0.0,<2.1|>=2.0.0,<2.0.1|>=2.0.0,<2.1.0']
matminer=0.7.3 -> requests[version='>=2.20.0'] -> charset-normalizer[version='>=2,<3|>=2.0.0,<2.1|>=2.0.0,<2.0.1|>=2.0.0,<2.1.0']
pyg=2.0.1 -> requests -> charset-normalizer[version='>=2,<3|>=2.0.0,<2.1|>=2.0.0,<2.0.1|>=2.0.0,<2.1.0']

Package pillow conflicts for:
seaborn -> matplotlib-base[version='>=2.1.2'] -> pillow[version='>=6.2.0']
matplotlib -> matplotlib-base[version='>=3.5.2,<3.5.3.0a0'] -> pillow[version='>=6.2.0']
ase=3.22 -> matplotlib-base -> pillow[version='>=6.2.0']
pymatgen=2020.12.31 -> matplotlib-base[version='>=1.5'] -> pillow[version='>=6.2.0']

Package pygments conflicts for:
jupyterlab -> ipython -> pygments[version='>=2.4.0']
ipywidgets -> ipython[version='>=4.0.0'] -> pygments[version='>=2.4.0']

Package colorama conflicts for:
pip -> colorama
jupyterlab -> ipython -> colorama
matminer=0.7.3 -> pip -> colorama
pymatgen=2020.12.31 -> pip -> colorama
tqdm -> colorama
pytorch-lightning=1.3.8 -> tqdm[version='>=4.41.0'] -> colorama
pylint -> colorama
pyg=2.0.1 -> tqdm -> colorama
python=3.8 -> pip -> colorama
ipywidgets -> ipython[version='>=4.0.0'] -> colorama

Package tqdm conflicts for:
matminer=0.7.3 -> tqdm[version='>=4.23.1']
matminer=0.7.3 -> mdf_forge[version='>=0.6.1'] -> tqdm[version='>=4.19.4']
pymatgen=2020.12.31 -> tqdm
pytorch-lightning=1.3.8 -> tqdm[version='>=4.41.0']
tqdm

Package nbformat conflicts for:
matminer=0.7.3 -> plotly[version='>=3.2.1'] -> nbformat[version='>=4.2']
jupyterlab -> jupyter_server[version='>=1.16,<2'] -> nbformat[version='>=5.2|>=5.2.0']
ipywidgets -> nbformat[version='>=4.2.0']
ipywidgets -> notebook -> nbformat
nglview -> ipywidgets[version='>=7'] -> nbformat[version='>=4.2.0']

Package pytz conflicts for:
matplotlib -> pytz
seaborn -> matplotlib-base[version='>=2.1.2'] -> pytz[version='>=2017.2|>=2020.1|>=2017.3']
matminer=0.7.3 -> pandas[version='>=0.23.4'] -> pytz[version='>=2017.2|>=2020.1|>=2017.3']
pymatgen=2020.12.31 -> apscheduler[version='>=2.1.0'] -> pytz[version='>=2017.2|>=2020.1|>=2017.3']
pyg=2.0.1 -> pandas -> pytz[version='>=2017.2|>=2020.1|>=2017.3']
ase=3.22 -> matplotlib-base -> pytz

Package progress conflicts for:
pip -> progress
matminer=0.7.3 -> pip -> progress
python=3.8 -> pip -> progress
pymatgen=2020.12.31 -> pip -> progress

Package pyqt conflicts for:
seaborn -> matplotlib[version='>=2.1.2'] -> pyqt[version='4.11.|5.|5.6.|5.9.|>=5.12.3,<5.13.0a0|>=5.9.2,<5.10.0a0|>=5.6.0,<5.7.0a0|>=5.6,<6.0a0']
matplotlib -> pyqt[version='4.11.|>=5.12.3,<5.13.0a0|>=5.9.2,<5.10.0a0|>=5.6.0,<5.7.0a0|5.9.|5.6.|>=5.6,<6.0a0|5.']

Package m2w64-gcc-libs conflicts for:
pytorch=1.8.1 -> blas=[build=mkl] -> m2w64-gcc-libs
ase=3.22 -> scipy -> m2w64-gcc-libs
pymatgen=2020.12.31 -> scipy[version='>=1.4.1'] -> m2w64-gcc-libs
seaborn -> scipy[version='>=1.0.1'] -> m2w64-gcc-libs

Package decorator conflicts for:
pyg=2.0.1 -> networkx[version='>=2.4'] -> decorator[version='>=4.3.0|>=4.3.0,<5']
pymatgen=2020.12.31 -> networkx[version='>=2.2'] -> decorator[version='>=3.4.0|>=4.3.0|>=4.3.0,<5']
jupyterlab -> ipython -> decorator
matminer=0.7.3 -> plotly[version='>=3.2.1'] -> decorator[version='>=4.0.6']
ipywidgets -> ipython[version='>=4.0.0'] -> decorator

Package networkx conflicts for:
pymatgen=2020.12.31 -> networkx[version='>=2.2']
pyg=2.0.1 -> networkx[version='>=2.4']
pyg=2.0.1 -> python-louvain -> networkx
matminer=0.7.3 -> pymatgen[version='>=2019.10.2'] -> networkx[version='>=2.2']

Package six conflicts for:
matminer=0.7.3 -> six[version='>=1.16.0']
matplotlib -> cycler -> six[version='>=1.5']
pylint -> six
pylint -> astroid==2.5.6 -> six[version='>=1.12,<2']
pip -> html5lib -> six[version='>=1.9']
matminer=0.7.3 -> aflow[version='>=0.0.9'] -> six[version='1.15.0|>=1.15.0']
seaborn -> patsy -> six
pymatgen=2020.12.31 -> apscheduler[version='>=2.1.0'] -> six[version='1.15.0|>=1.4.0|>=1.15.0']
jupyterlab -> packaging -> six
ipywidgets -> traitlets[version='>=4.3.1,<6.0.0'] -> six
pytorch-lightning=1.3.8 -> packaging -> six[version='>=1.10.0|>=1.12']

Package packaging conflicts for:
jupyterlab -> packaging
pytorch-lightning=1.3.8 -> packaging
pip -> packaging
matminer=0.7.3 -> pint[version='>=0.8.1'] -> packaging
nglview -> ipykernel -> packaging
seaborn -> statsmodels[version='>=0.8.0'] -> packaging[version='>=20.0|>=21.3']
ipywidgets -> ipykernel[version='>=4.5.1'] -> packaging
ase=3.22 -> matplotlib-base -> packaging[version='>=20.0']
python=3.8 -> pip -> packaging
pip -> wheel -> packaging[version='>=20.2']
pymatgen=2020.12.31 -> matplotlib-base[version='>=1.5'] -> packaging[version='>=20.0']
matplotlib -> matplotlib-base[version='>=3.5.2,<3.5.3.0a0'] -> packaging[version='>=20.0']

Package freetype conflicts for:
matplotlib -> freetype[version='2.6.*|>=2.9.1,<3.0a0|>=2.8,<2.9.0a0']
matplotlib -> matplotlib-base[version='>=3.5.2,<3.5.3.0a0'] -> freetype[version='>=2.10.2,<3.0a0|>=2.10.4,<3.0a0|>=2.3']

Package futures conflicts for:
pylint -> isort[version='>=4.2.5,<6'] -> futures
pymatgen=2020.12.31 -> apscheduler[version='>=2.1.0'] -> futures
jupyterlab -> futures
matplotlib -> tornado -> futures

Package toml conflicts for:
autopep8 -> toml
pylint -> toml[version='>=0.7.1|>=0.9.2']

Package jinja2 conflicts for:
pyg=2.0.1 -> jinja2
ase=3.22 -> flask -> jinja2[version='>=2.10|>=2.10.1|>=2.10.1,<3.0|>=3.0|>=2.4']
ipywidgets -> notebook -> jinja2
matminer=0.7.3 -> aflow[version='>=0.0.9'] -> jinja2
jupyterlab -> jupyter_server[version='>=1.16,<2'] -> jinja2[version='>2.10*|>=3.0.3']
nglview -> notebook -> jinja2
jupyterlab -> jinja2[version='>=2.1|>=2.10']

Package backports_abc conflicts for:
jupyterlab -> tornado[version='!=6.0.0,!=6.0.1,!=6.0.2'] -> backports_abc[version='>=0.4']
matplotlib -> tornado -> backports_abc[version='>=0.4']

Package importlib-metadata conflicts for:
ipywidgets -> ipykernel[version='>=4.5.1'] -> importlib-metadata[version='<4|<5']
nglview -> ipykernel -> importlib-metadata[version='<4|<5']
ase=3.22 -> flask -> importlib-metadata[version='>=3.6.0']

Package ca-certificates conflicts for:
nglview -> python -> ca-certificates
autopep8 -> python -> ca-certificates
jupyterlab -> python[version='>=2.7,<2.8.0a0'] -> ca-certificates
tqdm -> python[version='>=2.7'] -> ca-certificates
python=3.8 -> openssl[version='>=1.1.1n,<1.1.2a'] -> ca-certificates
pip -> python -> ca-certificates
seaborn -> python -> ca-certificates
pylint -> python[version='>=2.7,<2.8.0a0'] -> ca-certificates
ipywidgets -> python -> ca-certificates
matplotlib -> python[version='>=2.7,<2.8.0a0'] -> ca-certificates

Package intel-openmp conflicts for:
pyg=2.0.1 -> pytorch=1.9 -> intel-openmp
pytorch-lightning=1.3.8 -> pytorch[version='>=1.4'] -> intel-openmp
pytorch=1.8.1 -> mkl[version='>=2018'] -> intel-openmp[version='2021.|2022.']
pytorch=1.8.1 -> intel-openmp
pymatgen=2020.12.31 -> netcdf4 -> intel-openmp=2020.0

Package enum34 conflicts for:
pylint -> astroid[version='>=1.6,<2.0'] -> enum34
ipywidgets -> traitlets[version='>=4.3.1,<6.0.0'] -> enum34

Package ase conflicts for:
ase=3.22
matminer=0.7.3 -> ase[version='>=3.14.1']
pymatgen=2020.12.31 -> ase[version='>=3.3']
matminer=0.7.3 -> aflow[version='>=0.0.9'] -> ase[version='>=3.3']

Package pyyaml conflicts for:
matminer=0.7.3 -> citrination-client[version='>=4.0.1'] -> pyyaml
pyg=2.0.1 -> pyyaml
pytorch-lightning=1.3.8 -> pyyaml[version='>=5.1,<=5.4.1']
pymatgen=2020.12.31 -> pybtex -> pyyaml[version='>=3.01']

Package tornado conflicts for:
jupyterlab -> tornado[version='!=6.0.0,!=6.0.1,!=6.0.2|>=6.1|>=6.1.0']
pymatgen=2020.12.31 -> matplotlib-base[version='>=1.5'] -> tornado
ase=3.22 -> matplotlib-base -> tornado
seaborn -> matplotlib-base[version='>=2.1.2'] -> tornado
matplotlib -> tornado
ipywidgets -> ipykernel[version='>=4.5.1'] -> tornado[version='>=4|>=4,<6|>=4.0|>=4.2|>=4.2,<7.0|>=5.0,<7.0|>=6.1|>=5.0|>=5.0,<7|>=4.1,<7']
jupyterlab -> notebook[version='>=4.3.1'] -> tornado[version='>=4|>=4,<6|>=4.1,<7|>=5.0|>=5.0,<7']
nglview -> notebook -> tornado[version='>=4.0|>=4.2|>=4.2,<7.0|>=4|>=4,<6|>=4.1,<7|>=5.0,<7|>=5.0|>=6.1|>=5.0,<7.0']

Package singledispatch conflicts for:
jupyterlab -> tornado[version='!=6.0.0,!=6.0.1,!=6.0.2'] -> singledispatch
pylint -> singledispatch
matplotlib -> tornado -> singledispatch

Package certifi conflicts for:
pip -> setuptools -> certifi[version='>=2016.09|>=2016.9.26|>=2017.4.17']
pymatgen=2020.12.31 -> matplotlib-base[version='>=1.5'] -> certifi[version='>=2017.4.17|>=2020.06.20']
matminer=0.7.3 -> requests[version='>=2.20.0'] -> certifi[version='>=2017.4.17']
seaborn -> matplotlib-base[version='>=2.1.2'] -> certifi[version='>=2020.06.20']
pyg=2.0.1 -> requests -> certifi[version='>=2017.4.17']
matplotlib -> matplotlib-base[version='>=3.5.2,<3.5.3.0a0'] -> certifi[version='>=2016.09|>=2016.9.26|>=2020.06.20']
ase=3.22 -> matplotlib-base -> certifi[version='>=2020.06.20']
jupyterlab -> tornado[version='!=6.0.0,!=6.0.1,!=6.0.2'] -> certifi

Package ipykernel conflicts for:
ipywidgets -> ipykernel[version='>=4.2.2|>=4.5.1']
nglview -> ipykernel[version='<4.7']
jupyterlab -> notebook[version='>=4.3.1'] -> ipykernel
nglview -> ipywidgets[version='>=7'] -> ipykernel[version='>=4.2.2|>=4.5.1']

Package wheel conflicts for:
python=3.8 -> pip -> wheel
pymatgen=2020.12.31 -> pip -> wheel
matminer=0.7.3 -> pip -> wheel
pytorch-lightning=1.3.8 -> tensorboard[version='>=2.2.0,!=2.5.0'] -> wheel[version='>=0.26']
jupyterlab -> jupyter-packaging[version='>=0.7,<1'] -> wheel
pip -> wheel

Package backports conflicts for:
matplotlib -> backports.functools_lru_cache -> backports
pylint -> backports.functools_lru_cache -> backports

Package numexpr conflicts for:
matminer=0.7.3 -> pandas[version='>=0.23.4'] -> numexpr[version='>=2.6.8|>=2.7.0|>=2.7.1']
seaborn -> pandas[version='>=0.22.0'] -> numexpr[version='>=2.6.8|>=2.7.0|>=2.7.1']
pymatgen=2020.12.31 -> pandas -> numexpr[version='>=2.6.8|>=2.7.0|>=2.7.1']
pyg=2.0.1 -> pandas -> numexpr[version='>=2.6.8|>=2.7.0|>=2.7.1']

Package numpy-base conflicts for:
pyg=2.0.1 -> numpy -> numpy-base[version='1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.14.3|1.14.3|1.14.3|1.14.4|1.14.4|1.14.4|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.6|1.14.6|1.14.6|1.14.6|1.14.6|1.14.6|1.14.6|1.14.6|1.15.0|1.15.0|1.15.0|1.15.0|1.15.1|1.15.1|1.15.1|1.15.1|1.15.2|1.15.2|1.15.2|1.15.2|1.15.2|1.15.3|1.15.3|1.15.3|1.15.4|1.15.4|1.15.4|1.15.4|1.15.4|1.15.4|1.16.0|1.16.0|1.16.0|1.16.0|1.16.0|1.16.0|1.16.1|1.16.1|1.16.1|1.16.1|1.16.1|1.16.1|1.16.2|1.16.2|1.16.2|1.16.3|1.16.3|1.16.3|1.16.4|1.16.4|1.16.5|1.16.5|1.16.5|1.16.6|1.16.6|1.16.6|1.16.6|1.16.6|1.16.6|1.16.6|1.16.6|1.16.6|1.16.6|1.16.6|1.16.6|1.17.2.|1.17.3.|1.17.4.|1.18.1.|1.18.5.|1.19.1|1.19.1|1.19.1|1.19.2|1.19.2|1.19.2|1.19.2|1.19.2|1.19.2|1.19.2|1.20.1|1.20.1|1.20.1|1.20.2|1.20.2|1.20.2|1.20.3|1.20.3|1.20.3|1.21.2|1.21.2|1.21.2|1.21.2|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.22.3|1.9.3|1.9.3|1.9.3|1.9.3|1.9.3|1.9.3|1.9.3|>=1.9.3,<2.0a0|1.17.0|1.17.0',build='py37hc3f5095_0|py36hc3f5095_0|py37h5c71026_6|py27h0bb1d87_6|py36h5c71026_7|py37h5c71026_7|py37h5c71026_7|py37h5c71026_8|py27h0bb1d87_8|py35h4a99626_9|py36h4a99626_9|py37h4a99626_9|py27hfef472a_9|py37h8128ebf_9|py36h8128ebf_9|py35h8128ebf_9|py27h2753ae9_9|py35h8128ebf_10|py36h8128ebf_10|py36h8128ebf_11|py37h8128ebf_11|py37h2a9b21d_11|py27hb1d0314_11|py37hc3f5095_12|py36hc3f5095_12|py36h555522e_1|py36h5c71026_0|py27h0bb1d87_1|py37h5c71026_1|py37h5c71026_3|py36h5c71026_4|py35h4a99626_4|py36h8128ebf_4|py37h8128ebf_4|py27h2753ae9_4|py35h8128ebf_4|py37hc3f5095_5|py27hb1d0314_5|py35h4a99626_0|py36h4a99626_0|py37h4a99626_0|py37h8128ebf_0|py35h8128ebf_0|py36h8128ebf_0|py36h8128ebf_0|py37h8128ebf_0|py36h8128ebf_0|py37h8128ebf_0|py36h8128ebf_0|py27hb1d0314_0|py37hc3f5095_0|py37hc3f5095_0|py36hc3f5095_0|py37hc3f5095_1|py27hb1d0314_0|py37hc3f5095_0|py36hc3f5095_0|py37hc3f5095_1|py27hb1d0314_1|py37hc3f5095_0|py37hc3f5095_0|py36hc3f5095_0|py36hc3f5095_0|py37hc3f5095_0|py37hc3f5095_0|py37hc3f5095_0|py36h5bb6eb2_3|py39h378b42e_4|py38ha3acd2a_0|py37ha3acd2a_0|py36ha3acd2a_0|py38ha3acd2a_0|py37ha3acd2a_0|py36ha3acd2a_0|py39hbd0edd7_0|py37haf7ebc8_0|py38haf7ebc8_0|py39hc2deb75_0|py38hc2deb75_0|py37hc2deb75_0|py38hc2deb75_0|py39hc2deb75_0|py38h0829f74_0|py310hedd7904_0|py38hc2deb75_0|py37hca35cd5_1|py38hca35cd5_1|py39hca35cd5_1|py39hca35cd5_2|py310h206c741_2|py37hca35cd5_3|py38hca35cd5_3|py310h206c741_3|py39hca35cd5_0|py38hca35cd5_0|py310h206c741_0|py39hca35cd5_3|py37hca35cd5_2|py38hca35cd5_2|py310h206c741_1|py37hc2deb75_0|py39hc2deb75_0|py310h0829f74_0|py37h0829f74_0|py39h0829f74_0|py37hc2deb75_0|py39haf7ebc8_0|py38h5bb6eb2_1|py37h5bb6eb2_1|py39h5bb6eb2_1|py37h378b42e_4|py38h378b42e_4|py38h5bb6eb2_3|py39h5bb6eb2_3|py37h5bb6eb2_3|py39h2e04a8b_1|py27hb1d0314_0|py38hc3f5095_0|py36hc3f5095_0|py27hb1d0314_0|py36hc3f5095_0|py27hb1d0314_0|py36hc3f5095_0|py27hb1d0314_0|py36hc3f5095_1|py27hb1d0314_1|py36hc3f5095_1|py27hb1d0314_0|py36hc3f5095_0|py27h2753ae9_0|py37h8128ebf_0|py27h2753ae9_0|py27h2753ae9_1|py27h2753ae9_0|py35h8128ebf_0|py27h2753ae9_0|py27hfef472a_0|py36hc3f5095_5|py38hc3f5095_4|py37h5c71026_4|py27h0bb1d87_4|py36h5c71026_3|py27h0bb1d87_3|py27h0bb1d87_2|py37h5c71026_2|py36h5c71026_2|py36h5c71026_1|py37h5c71026_0|py36h5c71026_0|py27h0bb1d87_0|py35h5c71026_0|py27h0bb1d87_0|py35h5c71026_0|py27h917549b_1|py35h555522e_1|py38hc3f5095_12|py27hb1d0314_12|py36h2a9b21d_11|py27h2753ae9_10|py37h8128ebf_10|py35h4a99626_8|py36h5c71026_8|py36h5c71026_7|py27h0bb1d87_7|py35h5c71026_7|py27h0bb1d87_7|py36h5c71026_6']
ase=3.22 -> numpy -> numpy-base[version='1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.14.3|1.14.3|1.14.3|1.14.4|1.14.4|1.14.4|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.6|1.14.6|1.14.6|1.14.6|1.14.6|1.14.6|1.14.6|1.14.6|1.15.0|1.15.0|1.15.0|1.15.0|1.15.1|1.15.1|1.15.1|1.15.1|1.15.2|1.15.2|1.15.2|1.15.2|1.15.2|1.15.3|1.15.3|1.15.3|1.15.4|1.15.4|1.15.4|1.15.4|1.15.4|1.15.4|1.16.0|1.16.0|1.16.0|1.16.0|1.16.0|1.16.0|1.16.1|1.16.1|1.16.1|1.16.1|1.16.1|1.16.1|1.16.2|1.16.2|1.16.2|1.16.3|1.16.3|1.16.3|1.16.4|1.16.4|1.16.5|1.16.5|1.16.5|1.16.6|1.16.6|1.16.6|1.16.6|1.16.6|1.16.6|1.16.6|1.16.6|1.16.6|1.16.6|1.16.6|1.16.6|1.17.2.|1.17.3.|1.17.4.|1.18.1.|1.18.5.|1.19.1|1.19.1|1.19.1|1.19.2|1.19.2|1.19.2|1.19.2|1.19.2|1.19.2|1.19.2|1.20.1|1.20.1|1.20.1|1.20.2|1.20.2|1.20.2|1.20.3|1.20.3|1.20.3|1.21.2|1.21.2|1.21.2|1.21.2|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.22.3|1.9.3|1.9.3|1.9.3|1.9.3|1.9.3|1.9.3|1.9.3|>=1.9.3,<2.0a0|1.17.0|1.17.0',build='py37hc3f5095_0|py36hc3f5095_0|py37h5c71026_6|py27h0bb1d87_6|py36h5c71026_7|py37h5c71026_7|py37h5c71026_7|py37h5c71026_8|py27h0bb1d87_8|py35h4a99626_9|py36h4a99626_9|py37h4a99626_9|py27hfef472a_9|py37h8128ebf_9|py36h8128ebf_9|py35h8128ebf_9|py27h2753ae9_9|py35h8128ebf_10|py36h8128ebf_10|py36h8128ebf_11|py37h8128ebf_11|py37h2a9b21d_11|py27hb1d0314_11|py37hc3f5095_12|py36hc3f5095_12|py36h555522e_1|py36h5c71026_0|py27h0bb1d87_1|py37h5c71026_1|py37h5c71026_3|py36h5c71026_4|py35h4a99626_4|py36h8128ebf_4|py37h8128ebf_4|py27h2753ae9_4|py35h8128ebf_4|py37hc3f5095_5|py27hb1d0314_5|py35h4a99626_0|py36h4a99626_0|py37h4a99626_0|py37h8128ebf_0|py35h8128ebf_0|py36h8128ebf_0|py36h8128ebf_0|py37h8128ebf_0|py36h8128ebf_0|py37h8128ebf_0|py36h8128ebf_0|py27hb1d0314_0|py37hc3f5095_0|py37hc3f5095_0|py36hc3f5095_0|py37hc3f5095_1|py27hb1d0314_0|py37hc3f5095_0|py36hc3f5095_0|py37hc3f5095_1|py27hb1d0314_1|py37hc3f5095_0|py37hc3f5095_0|py36hc3f5095_0|py36hc3f5095_0|py37hc3f5095_0|py37hc3f5095_0|py37hc3f5095_0|py36h5bb6eb2_3|py39h378b42e_4|py38ha3acd2a_0|py37ha3acd2a_0|py36ha3acd2a_0|py38ha3acd2a_0|py37ha3acd2a_0|py36ha3acd2a_0|py39hbd0edd7_0|py37haf7ebc8_0|py38haf7ebc8_0|py39hc2deb75_0|py38hc2deb75_0|py37hc2deb75_0|py38hc2deb75_0|py39hc2deb75_0|py38h0829f74_0|py310hedd7904_0|py38hc2deb75_0|py37hca35cd5_1|py38hca35cd5_1|py39hca35cd5_1|py39hca35cd5_2|py310h206c741_2|py37hca35cd5_3|py38hca35cd5_3|py310h206c741_3|py39hca35cd5_0|py38hca35cd5_0|py310h206c741_0|py39hca35cd5_3|py37hca35cd5_2|py38hca35cd5_2|py310h206c741_1|py37hc2deb75_0|py39hc2deb75_0|py310h0829f74_0|py37h0829f74_0|py39h0829f74_0|py37hc2deb75_0|py39haf7ebc8_0|py38h5bb6eb2_1|py37h5bb6eb2_1|py39h5bb6eb2_1|py37h378b42e_4|py38h378b42e_4|py38h5bb6eb2_3|py39h5bb6eb2_3|py37h5bb6eb2_3|py39h2e04a8b_1|py27hb1d0314_0|py38hc3f5095_0|py36hc3f5095_0|py27hb1d0314_0|py36hc3f5095_0|py27hb1d0314_0|py36hc3f5095_0|py27hb1d0314_0|py36hc3f5095_1|py27hb1d0314_1|py36hc3f5095_1|py27hb1d0314_0|py36hc3f5095_0|py27h2753ae9_0|py37h8128ebf_0|py27h2753ae9_0|py27h2753ae9_1|py27h2753ae9_0|py35h8128ebf_0|py27h2753ae9_0|py27hfef472a_0|py36hc3f5095_5|py38hc3f5095_4|py37h5c71026_4|py27h0bb1d87_4|py36h5c71026_3|py27h0bb1d87_3|py27h0bb1d87_2|py37h5c71026_2|py36h5c71026_2|py36h5c71026_1|py37h5c71026_0|py36h5c71026_0|py27h0bb1d87_0|py35h5c71026_0|py27h0bb1d87_0|py35h5c71026_0|py27h917549b_1|py35h555522e_1|py38hc3f5095_12|py27hb1d0314_12|py36h2a9b21d_11|py27h2753ae9_10|py37h8128ebf_10|py35h4a99626_8|py36h5c71026_8|py36h5c71026_7|py27h0bb1d87_7|py35h5c71026_7|py27h0bb1d87_7|py36h5c71026_6']
pytorch=1.8.1 -> numpy[version='>=1.19'] -> numpy-base[version='1.19.1|1.19.1|1.19.1|1.19.2|1.19.2|1.19.2|1.19.2|1.19.2|1.19.2|1.19.2|1.20.1|1.20.1|1.20.1|1.20.2|1.20.2|1.20.2|1.20.3|1.20.3|1.20.3|1.21.2|1.21.2|1.21.2|1.21.2|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.22.3',build='py38ha3acd2a_0|py37ha3acd2a_0|py36ha3acd2a_0|py38ha3acd2a_0|py37ha3acd2a_0|py36ha3acd2a_0|py39hbd0edd7_0|py37haf7ebc8_0|py38haf7ebc8_0|py39hc2deb75_0|py38hc2deb75_0|py37hc2deb75_0|py38hc2deb75_0|py39hc2deb75_0|py38h0829f74_0|py310hedd7904_0|py38hc2deb75_0|py37hca35cd5_1|py38hca35cd5_1|py39hca35cd5_1|py39hca35cd5_2|py310h206c741_2|py37hca35cd5_3|py38hca35cd5_3|py310h206c741_3|py39hca35cd5_0|py38hca35cd5_0|py310h206c741_0|py39hca35cd5_3|py37hca35cd5_2|py38hca35cd5_2|py310h206c741_1|py37hc2deb75_0|py39hc2deb75_0|py310h0829f74_0|py37h0829f74_0|py39h0829f74_0|py37hc2deb75_0|py39haf7ebc8_0|py38h5bb6eb2_1|py37h5bb6eb2_1|py39h5bb6eb2_1']
matplotlib -> numpy=1.11 -> numpy-base[version='1.11.3|1.22.3|1.22.3|1.22.3|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.2|1.21.2|1.21.2|1.21.2|1.20.3|1.20.3|1.20.3|1.20.2|1.20.2|1.20.2|1.20.1|1.20.1|1.20.1|1.19.2|1.19.2|1.19.2|1.19.2|1.19.2|1.19.2|1.19.2|1.19.1|1.19.1|1.19.1|1.18.5.|1.18.1.|1.17.4.|1.17.3.|1.17.2.|1.16.6|1.16.6|1.16.6|1.16.6|1.16.6|1.16.6|1.16.6|1.16.6|1.16.6|1.16.6|1.16.6|1.16.6|1.16.5|1.16.5|1.16.5|1.16.4|1.16.4|1.16.3|1.16.3|1.16.3|1.16.2|1.16.2|1.16.2|1.16.1|1.16.1|1.16.1|1.16.1|1.16.1|1.16.1|1.16.0|1.16.0|1.16.0|1.16.0|1.16.0|1.16.0|1.15.4|1.15.4|1.15.4|1.15.4|1.15.4|1.15.4|1.15.3|1.15.3|1.15.3|1.15.2|1.15.2|1.15.2|1.15.2|1.15.2|1.15.1|1.15.1|1.15.1|1.15.1|1.15.0|1.15.0|1.15.0|1.15.0|1.14.6|1.14.6|1.14.6|1.14.6|1.14.6|1.14.6|1.14.6|1.14.6|1.17.0|1.17.0|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.4|1.14.4|1.14.4|1.14.3|1.14.3|1.14.3|1.9.3|1.9.3|1.9.3|1.9.3|1.9.3|1.9.3|1.9.3|>=1.9.3,<2.0a0',build='py37h5c71026_6|py36h5c71026_6|py27h0bb1d87_6|py37h5c71026_7|py35h555522e_1|py36h555522e_1|py27h0bb1d87_0|py27h0bb1d87_1|py36h5c71026_1|py36h5c71026_3|py37h5c71026_3|py27h0bb1d87_4|py36h5c71026_4|py35h4a99626_4|py37hc3f5095_0|py36hc3f5095_0|py36h8128ebf_4|py37h8128ebf_4|py27h2753ae9_4|py35h8128ebf_4|py38hc3f5095_4|py37hc3f5095_5|py27hb1d0314_5|py35h4a99626_0|py36h4a99626_0|py37h4a99626_0|py37h8128ebf_0|py36h8128ebf_0|py35h8128ebf_0|py37h8128ebf_0|py27h2753ae9_1|py36h8128ebf_0|py37h8128ebf_0|py36h8128ebf_0|py36hc3f5095_0|py37hc3f5095_0|py37hc3f5095_0|py27hb1d0314_0|py37hc3f5095_1|py27hb1d0314_1|py37hc3f5095_0|py36hc3f5095_0|py37hc3f5095_1|py27hb1d0314_0|py37hc3f5095_0|py36hc3f5095_0|py37hc3f5095_0|py36hc3f5095_0|py27hb1d0314_0|py37hc3f5095_0|py36hc3f5095_0|py37hc3f5095_0|py36hc3f5095_0|py37hc3f5095_0|py27hb1d0314_0|py38ha3acd2a_0|py36ha3acd2a_0|py38ha3acd2a_0|py37ha3acd2a_0|py39hbd0edd7_0|py39hc2deb75_0|py38hc2deb75_0|py38h0829f74_0|py39hc2deb75_0|py38hc2deb75_0|py38hca35cd5_1|py39hca35cd5_1|py310h206c741_1|py38hca35cd5_2|py310h206c741_2|py37hca35cd5_3|py38hca35cd5_3|py310h206c741_3|py310h206c741_0|py38hca35cd5_0|py37h5c71026_7|py37h5c71026_8|py27h0bb1d87_8|py35h4a99626_9|py36h4a99626_9|py37h8128ebf_9|py35h8128ebf_9|py36h8128ebf_10|py27h2753ae9_10|py36h8128ebf_11|py37h8128ebf_11|py37h2a9b21d_11|py27hb1d0314_11|py37hc3f5095_12|py36hc3f5095_12|py38hc3f5095_12|py27hb1d0314_12|py36h2a9b21d_11|py37h8128ebf_10|py35h8128ebf_10|py27h2753ae9_9|py36h8128ebf_9|py27hfef472a_9|py37h4a99626_9|py35h4a99626_8|py36h5c71026_8|py36h5c71026_7|py27h0bb1d87_7|py39hca35cd5_0|py39hca35cd5_3|py37hca35cd5_2|py39hca35cd5_2|py37hca35cd5_1|py37hc2deb75_0|py310hedd7904_0|py310h0829f74_0|py37h0829f74_0|py39h0829f74_0|py39hc2deb75_0|py38hc2deb75_0|py37hc2deb75_0|py37hc2deb75_0|py38haf7ebc8_0|py39haf7ebc8_0|py37haf7ebc8_0|py38h5bb6eb2_1|py37h5bb6eb2_1|py39h5bb6eb2_1|py36ha3acd2a_0|py37ha3acd2a_0|py37h378b42e_4|py39h378b42e_4|py38h378b42e_4|py38h5bb6eb2_3|py36h5bb6eb2_3|py39h5bb6eb2_3|py37h5bb6eb2_3|py39h2e04a8b_1|py38hc3f5095_0|py27hb1d0314_0|py36hc3f5095_0|py27hb1d0314_1|py36hc3f5095_1|py27hb1d0314_0|py36hc3f5095_1|py36hc3f5095_0|py27hb1d0314_0|py27h2753ae9_0|py27h2753ae9_0|py37h8128ebf_0|py36h8128ebf_0|py27h2753ae9_0|py27h2753ae9_0|py35h8128ebf_0|py27hfef472a_0|py36hc3f5095_5|py37h5c71026_4|py27h0bb1d87_3|py27h0bb1d87_2|py37h5c71026_2|py36h5c71026_2|py37h5c71026_1|py37h5c71026_0|py36h5c71026_0|py35h5c71026_0|py27h0bb1d87_0|py36h5c71026_0|py35h5c71026_0|py27h917549b_1|py35h5c71026_7|py36h5c71026_7|py27h0bb1d87_7']
nglview -> numpy -> numpy-base[version='1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.14.3|1.14.3|1.14.3|1.14.4|1.14.4|1.14.4|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.6|1.14.6|1.14.6|1.14.6|1.14.6|1.14.6|1.14.6|1.14.6|1.15.0|1.15.0|1.15.0|1.15.0|1.15.1|1.15.1|1.15.1|1.15.1|1.15.2|1.15.2|1.15.2|1.15.2|1.15.2|1.15.3|1.15.3|1.15.3|1.15.4|1.15.4|1.15.4|1.15.4|1.15.4|1.15.4|1.16.0|1.16.0|1.16.0|1.16.0|1.16.0|1.16.0|1.16.1|1.16.1|1.16.1|1.16.1|1.16.1|1.16.1|1.16.2|1.16.2|1.16.2|1.16.3|1.16.3|1.16.3|1.16.4|1.16.4|1.16.5|1.16.5|1.16.5|1.16.6|1.16.6|1.16.6|1.16.6|1.16.6|1.16.6|1.16.6|1.16.6|1.16.6|1.16.6|1.16.6|1.16.6|1.17.2.|1.17.3.|1.17.4.|1.18.1.|1.18.5.|1.19.1|1.19.1|1.19.1|1.19.2|1.19.2|1.19.2|1.19.2|1.19.2|1.19.2|1.19.2|1.20.1|1.20.1|1.20.1|1.20.2|1.20.2|1.20.2|1.20.3|1.20.3|1.20.3|1.21.2|1.21.2|1.21.2|1.21.2|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.22.3|1.9.3|1.9.3|1.9.3|1.9.3|1.9.3|1.9.3|1.9.3|>=1.9.3,<2.0a0|1.17.0|1.17.0',build='py37hc3f5095_0|py36hc3f5095_0|py37h5c71026_6|py27h0bb1d87_6|py36h5c71026_7|py37h5c71026_7|py37h5c71026_7|py37h5c71026_8|py27h0bb1d87_8|py35h4a99626_9|py36h4a99626_9|py37h4a99626_9|py27hfef472a_9|py37h8128ebf_9|py36h8128ebf_9|py35h8128ebf_9|py27h2753ae9_9|py35h8128ebf_10|py36h8128ebf_10|py36h8128ebf_11|py37h8128ebf_11|py37h2a9b21d_11|py27hb1d0314_11|py37hc3f5095_12|py36hc3f5095_12|py36h555522e_1|py36h5c71026_0|py27h0bb1d87_1|py37h5c71026_1|py37h5c71026_3|py36h5c71026_4|py35h4a99626_4|py36h8128ebf_4|py37h8128ebf_4|py27h2753ae9_4|py35h8128ebf_4|py37hc3f5095_5|py27hb1d0314_5|py35h4a99626_0|py36h4a99626_0|py37h4a99626_0|py37h8128ebf_0|py35h8128ebf_0|py36h8128ebf_0|py36h8128ebf_0|py37h8128ebf_0|py36h8128ebf_0|py37h8128ebf_0|py36h8128ebf_0|py27hb1d0314_0|py37hc3f5095_0|py37hc3f5095_0|py36hc3f5095_0|py37hc3f5095_1|py27hb1d0314_0|py37hc3f5095_0|py36hc3f5095_0|py37hc3f5095_1|py27hb1d0314_1|py37hc3f5095_0|py37hc3f5095_0|py36hc3f5095_0|py36hc3f5095_0|py37hc3f5095_0|py37hc3f5095_0|py37hc3f5095_0|py36h5bb6eb2_3|py39h378b42e_4|py38ha3acd2a_0|py37ha3acd2a_0|py36ha3acd2a_0|py38ha3acd2a_0|py37ha3acd2a_0|py36ha3acd2a_0|py39hbd0edd7_0|py37haf7ebc8_0|py38haf7ebc8_0|py39hc2deb75_0|py38hc2deb75_0|py37hc2deb75_0|py38hc2deb75_0|py39hc2deb75_0|py38h0829f74_0|py310hedd7904_0|py38hc2deb75_0|py37hca35cd5_1|py38hca35cd5_1|py39hca35cd5_1|py39hca35cd5_2|py310h206c741_2|py37hca35cd5_3|py38hca35cd5_3|py310h206c741_3|py39hca35cd5_0|py38hca35cd5_0|py310h206c741_0|py39hca35cd5_3|py37hca35cd5_2|py38hca35cd5_2|py310h206c741_1|py37hc2deb75_0|py39hc2deb75_0|py310h0829f74_0|py37h0829f74_0|py39h0829f74_0|py37hc2deb75_0|py39haf7ebc8_0|py38h5bb6eb2_1|py37h5bb6eb2_1|py39h5bb6eb2_1|py37h378b42e_4|py38h378b42e_4|py38h5bb6eb2_3|py39h5bb6eb2_3|py37h5bb6eb2_3|py39h2e04a8b_1|py27hb1d0314_0|py38hc3f5095_0|py36hc3f5095_0|py27hb1d0314_0|py36hc3f5095_0|py27hb1d0314_0|py36hc3f5095_0|py27hb1d0314_0|py36hc3f5095_1|py27hb1d0314_1|py36hc3f5095_1|py27hb1d0314_0|py36hc3f5095_0|py27h2753ae9_0|py37h8128ebf_0|py27h2753ae9_0|py27h2753ae9_1|py27h2753ae9_0|py35h8128ebf_0|py27h2753ae9_0|py27hfef472a_0|py36hc3f5095_5|py38hc3f5095_4|py37h5c71026_4|py27h0bb1d87_4|py36h5c71026_3|py27h0bb1d87_3|py27h0bb1d87_2|py37h5c71026_2|py36h5c71026_2|py36h5c71026_1|py37h5c71026_0|py36h5c71026_0|py27h0bb1d87_0|py35h5c71026_0|py27h0bb1d87_0|py35h5c71026_0|py27h917549b_1|py35h555522e_1|py38hc3f5095_12|py27hb1d0314_12|py36h2a9b21d_11|py27h2753ae9_10|py37h8128ebf_10|py35h4a99626_8|py36h5c71026_8|py36h5c71026_7|py27h0bb1d87_7|py35h5c71026_7|py27h0bb1d87_7|py36h5c71026_6']
pytorch-lightning=1.3.8 -> numpy[version='>=1.17.2'] -> numpy-base[version='1.17.2.|1.17.3.|1.17.4.|1.18.1.|1.18.5.|1.19.1|1.19.1|1.19.1|1.19.2|1.19.2|1.19.2|1.19.2|1.19.2|1.19.2|1.19.2|1.20.1|1.20.1|1.20.1|1.20.2|1.20.2|1.20.2|1.20.3|1.20.3|1.20.3|1.21.2|1.21.2|1.21.2|1.21.2|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.22.3',build='py38ha3acd2a_0|py37ha3acd2a_0|py36ha3acd2a_0|py38ha3acd2a_0|py37ha3acd2a_0|py36ha3acd2a_0|py39hbd0edd7_0|py37haf7ebc8_0|py38haf7ebc8_0|py39hc2deb75_0|py38hc2deb75_0|py37hc2deb75_0|py38hc2deb75_0|py39hc2deb75_0|py38h0829f74_0|py310hedd7904_0|py38hc2deb75_0|py37hca35cd5_1|py38hca35cd5_1|py39hca35cd5_1|py39hca35cd5_2|py310h206c741_2|py37hca35cd5_3|py38hca35cd5_3|py310h206c741_3|py39hca35cd5_0|py38hca35cd5_0|py310h206c741_0|py39hca35cd5_3|py37hca35cd5_2|py38hca35cd5_2|py310h206c741_1|py37hc2deb75_0|py39hc2deb75_0|py310h0829f74_0|py37h0829f74_0|py39h0829f74_0|py37hc2deb75_0|py39haf7ebc8_0|py38h5bb6eb2_1|py37h5bb6eb2_1|py39h5bb6eb2_1']
pymatgen=2020.12.31 -> numpy[version='>=1.19.4,<2.0a0'] -> numpy-base[version='1.20.1|1.20.1|1.20.1|1.20.2|1.20.2|1.20.2|1.20.3|1.20.3|1.20.3|1.21.2|1.21.2|1.21.2|1.21.2|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.22.3',build='py37haf7ebc8_0|py38haf7ebc8_0|py39hc2deb75_0|py38hc2deb75_0|py37hc2deb75_0|py38hc2deb75_0|py39hc2deb75_0|py38h0829f74_0|py310hedd7904_0|py38hc2deb75_0|py37hca35cd5_1|py38hca35cd5_1|py39hca35cd5_1|py39hca35cd5_2|py310h206c741_2|py37hca35cd5_3|py38hca35cd5_3|py310h206c741_3|py39hca35cd5_0|py38hca35cd5_0|py310h206c741_0|py39hca35cd5_3|py37hca35cd5_2|py38hca35cd5_2|py310h206c741_1|py37hc2deb75_0|py39hc2deb75_0|py310h0829f74_0|py37h0829f74_0|py39h0829f74_0|py37hc2deb75_0|py39haf7ebc8_0']
seaborn -> numpy[version='>=1.13.3'] -> numpy-base[version='1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.11.3|1.14.3|1.14.3|1.14.3|1.14.4|1.14.4|1.14.4|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.6|1.14.6|1.14.6|1.14.6|1.14.6|1.14.6|1.14.6|1.14.6|1.15.0|1.15.0|1.15.0|1.15.0|1.15.1|1.15.1|1.15.1|1.15.1|1.15.2|1.15.2|1.15.2|1.15.2|1.15.2|1.15.3|1.15.3|1.15.3|1.15.4|1.15.4|1.15.4|1.15.4|1.15.4|1.15.4|1.16.0|1.16.0|1.16.0|1.16.0|1.16.0|1.16.0|1.16.1|1.16.1|1.16.1|1.16.1|1.16.1|1.16.1|1.16.2|1.16.2|1.16.2|1.16.3|1.16.3|1.16.3|1.16.4|1.16.4|1.16.5|1.16.5|1.16.5|1.16.6|1.16.6|1.16.6|1.16.6|1.16.6|1.16.6|1.16.6|1.16.6|1.16.6|1.16.6|1.16.6|1.16.6|1.17.2.|1.17.3.|1.17.4.|1.18.1.|1.18.5.|1.19.1|1.19.1|1.19.1|1.19.2|1.19.2|1.19.2|1.19.2|1.19.2|1.19.2|1.19.2|1.20.1|1.20.1|1.20.1|1.20.2|1.20.2|1.20.2|1.20.3|1.20.3|1.20.3|1.21.2|1.21.2|1.21.2|1.21.2|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.22.3|1.17.0|1.17.0|1.9.3|1.9.3|1.9.3|1.9.3|1.9.3|1.9.3|1.9.3|>=1.9.3,<2.0a0',build='py37h5c71026_6|py36h5c71026_6|py37h5c71026_7|py36h5c71026_7|py37h5c71026_8|py27h0bb1d87_8|py35h4a99626_9|py36h4a99626_9|py27hfef472a_9|py37h8128ebf_9|py35h8128ebf_10|py36h8128ebf_10|py27h2753ae9_10|py36h8128ebf_11|py37h8128ebf_11|py37h2a9b21d_11|py27hb1d0314_11|py37hc3f5095_12|py36hc3f5095_12|py37hc3f5095_0|py36h555522e_1|py36h5c71026_0|py27h0bb1d87_1|py37h5c71026_1|py37h5c71026_3|py36h5c71026_4|py35h4a99626_4|py36h8128ebf_4|py37h8128ebf_4|py27h2753ae9_4|py35h8128ebf_4|py37hc3f5095_5|py27hb1d0314_5|py35h4a99626_0|py36h4a99626_0|py37h4a99626_0|py37h8128ebf_0|py35h8128ebf_0|py36h8128ebf_0|py36h8128ebf_0|py37h8128ebf_0|py36h8128ebf_0|py37h8128ebf_0|py36h8128ebf_0|py27hb1d0314_0|py37hc3f5095_0|py37hc3f5095_0|py36hc3f5095_0|py37hc3f5095_1|py27hb1d0314_0|py37hc3f5095_0|py36hc3f5095_0|py37hc3f5095_1|py27hb1d0314_1|py37hc3f5095_0|py37hc3f5095_0|py36hc3f5095_0|py36hc3f5095_0|py37hc3f5095_0|py37hc3f5095_0|py37hc3f5095_0|py36h5bb6eb2_3|py39h378b42e_4|py38ha3acd2a_0|py37ha3acd2a_0|py36ha3acd2a_0|py38ha3acd2a_0|py37ha3acd2a_0|py36ha3acd2a_0|py39hbd0edd7_0|py37haf7ebc8_0|py38haf7ebc8_0|py39hc2deb75_0|py38hc2deb75_0|py37hc2deb75_0|py38hc2deb75_0|py39hc2deb75_0|py38h0829f74_0|py310hedd7904_0|py38hc2deb75_0|py37hca35cd5_1|py38hca35cd5_1|py39hca35cd5_1|py39hca35cd5_2|py310h206c741_2|py37hca35cd5_3|py38hca35cd5_3|py310h206c741_3|py39hca35cd5_0|py38hca35cd5_0|py310h206c741_0|py39hca35cd5_3|py37hca35cd5_2|py38hca35cd5_2|py310h206c741_1|py37hc2deb75_0|py39hc2deb75_0|py310h0829f74_0|py37h0829f74_0|py39h0829f74_0|py37hc2deb75_0|py39haf7ebc8_0|py38h5bb6eb2_1|py37h5bb6eb2_1|py39h5bb6eb2_1|py37h378b42e_4|py38h378b42e_4|py38h5bb6eb2_3|py39h5bb6eb2_3|py37h5bb6eb2_3|py39h2e04a8b_1|py27hb1d0314_0|py38hc3f5095_0|py36hc3f5095_0|py27hb1d0314_0|py36hc3f5095_0|py27hb1d0314_0|py36hc3f5095_0|py27hb1d0314_0|py36hc3f5095_1|py27hb1d0314_1|py36hc3f5095_1|py27hb1d0314_0|py36hc3f5095_0|py27h2753ae9_0|py37h8128ebf_0|py27h2753ae9_0|py27h2753ae9_1|py27h2753ae9_0|py35h8128ebf_0|py27h2753ae9_0|py27hfef472a_0|py36hc3f5095_5|py38hc3f5095_4|py37h5c71026_4|py27h0bb1d87_4|py36h5c71026_3|py27h0bb1d87_3|py27h0bb1d87_2|py37h5c71026_2|py36h5c71026_2|py36h5c71026_1|py37h5c71026_0|py36h5c71026_0|py27h0bb1d87_0|py35h5c71026_0|py27h0bb1d87_0|py35h5c71026_0|py27h917549b_1|py35h555522e_1|py36hc3f5095_0|py38hc3f5095_12|py27hb1d0314_12|py36h2a9b21d_11|py37h8128ebf_10|py27h2753ae9_9|py35h8128ebf_9|py36h8128ebf_9|py37h4a99626_9|py35h4a99626_8|py36h5c71026_8|py37h5c71026_7|py27h0bb1d87_7|py35h5c71026_7|py36h5c71026_7|py27h0bb1d87_7|py27h0bb1d87_6']
matminer=0.7.3 -> numpy[version='>=1.18.3'] -> numpy-base[version='1.18.5.*|1.19.1|1.19.1|1.19.1|1.19.2|1.19.2|1.19.2|1.19.2|1.19.2|1.19.2|1.19.2|1.20.1|1.20.1|1.20.1|1.20.2|1.20.2|1.20.2|1.20.3|1.20.3|1.20.3|1.21.2|1.21.2|1.21.2|1.21.2|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.21.5|1.22.3',build='py38ha3acd2a_0|py37ha3acd2a_0|py36ha3acd2a_0|py38ha3acd2a_0|py37ha3acd2a_0|py36ha3acd2a_0|py39hbd0edd7_0|py37haf7ebc8_0|py38haf7ebc8_0|py39hc2deb75_0|py38hc2deb75_0|py37hc2deb75_0|py38hc2deb75_0|py39hc2deb75_0|py38h0829f74_0|py310hedd7904_0|py38hc2deb75_0|py37hca35cd5_1|py38hca35cd5_1|py39hca35cd5_1|py39hca35cd5_2|py310h206c741_2|py37hca35cd5_3|py38hca35cd5_3|py310h206c741_3|py39hca35cd5_0|py38hca35cd5_0|py310h206c741_0|py39hca35cd5_3|py37hca35cd5_2|py38hca35cd5_2|py310h206c741_1|py37hc2deb75_0|py39hc2deb75_0|py310h0829f74_0|py37h0829f74_0|py39h0829f74_0|py37hc2deb75_0|py39haf7ebc8_0|py38h5bb6eb2_1|py37h5bb6eb2_1|py39h5bb6eb2_1']

Package matplotlib-base conflicts for:
seaborn -> matplotlib-base[version='>=2.1.2']
seaborn -> seaborn-base[version='>=0.11.2,<0.11.3.0a0'] -> matplotlib-base[version='2.1.2|2.1.2|2.1.2|2.2.3|2.2.3|2.2.3|2.2.4|2.2.4|2.2.4|2.2.4|2.2.4|2.2.4|2.2.4|2.2.4|2.2.4|2.2.4|3.0.2|3.0.2|3.0.3|3.0.3|3.0.3|3.0.3|3.1.0|3.1.0|3.1.0|3.1.0|3.1.1|3.1.1|3.1.1|3.1.1|3.1.1|3.1.1|3.1.1|3.1.1|3.1.2|3.1.2|3.1.2|3.1.2|3.1.2|3.1.2|3.1.3|3.1.3|3.1.3|>=2.2|>=3.5.2,<3.5.3.0a0|>=3.5.1,<3.5.2.0a0|>=3.5.0,<3.5.1.0a0|>=3.4.3,<3.4.4.0a0|>=3.4.2,<3.4.3.0a0|>=3.4.1,<3.4.2.0a0|>=3.3.4,<3.3.5.0a0|>=3.3.3,<3.3.4.0a0|>=3.3.2,<3.3.3.0a0|>=3.3.1,<3.3.2.0a0|>=3.3.0,<3.3.1.0a0|>=3.2.2,<3.2.3.0a0|>=3.2.1,<3.2.2.0a0|>=3.2.0,<3.2.1.0a0|>=2.2.5,<2.2.6.0a0|3.1.3|3.1.3|3.1.3|3.1.2|3.1.2|3.1.2',build='py37h64f37c6_1|py37h64f37c6_0|py27h6595424_1|py36h2981e6d_1|py27he27c676_0|py37h2852a4a_0|py27h6595424_1|py36h3e3dc42_0|py37h3e3dc42_0|py37h3e3dc42_0|py37h2852a4a_0|py37h2852a4a_1|py38h2981e6d_1|py36h2981e6d_0|py37h2981e6d_0|py38h2981e6d_0|py36h2981e6d_1|py38h2981e6d_1|py36h2981e6d_0|py38h2981e6d_0|py37h2981e6d_0|py37h2981e6d_1|py37h2981e6d_2|py36h2981e6d_2|py38h2981e6d_2|py36h2852a4a_1|py36h2852a4a_0|py36h2852a4a_1|py37h2852a4a_1|py36h3e3dc42_0|py37h3e3dc42_1|py36h3e3dc42_1|py37h3e3dc42_1002|py36h3e3dc42_1002|py27h6595424_2|py37h2981e6d_2|py36h2981e6d_2|py38h2981e6d_2|py36h2981e6d_1|py37h2981e6d_1|py36h2852a4a_0|py37ha47f3eb_1|py36ha47f3eb_1|py27hf194043_1|py37h2981e6d_1|py38h64f37c6_0|py36h64f37c6_0|py36h64f37c6_1|py38h64f37c6_1']

Package wincertstore conflicts for:
pip -> setuptools -> wincertstore[version='>=0.2']
matplotlib -> setuptools -> wincertstore[version='>=0.2']

Package pandas conflicts for:
seaborn -> pandas[version='>=0.14.0|>=0.22.0|>=0.23']
seaborn -> statsmodels[version='>=0.8.0'] -> pandas[version='>=0.14|>=0.21|>=1.0']

Package markupsafe conflicts for:
pyg=2.0.1 -> jinja2 -> markupsafe[version='>=0.23|>=0.23,<2|>=0.23,<2.1|>=2.0|>=2.0.0rc2']
jupyterlab -> jinja2[version='>=2.10'] -> markupsafe[version='>=0.23|>=0.23,<2|>=0.23,<2.1|>=2.0|>=2.0.0rc2']

Package typing conflicts for:
pymatgen=2020.12.31 -> ruamel.yaml[version='>=0.15.6'] -> typing
pytorch=1.8.1 -> typing_extensions -> typing[version='>=3.6.2|>=3.7.4']
pylint -> tomlkit[version='>=0.10.1'] -> typing[version='>=3.6,<4.0|>=3.7.4|>=3.6.2']

Package backports.functools_lru_cache conflicts for:
seaborn -> matplotlib-base[version='>=2.1.2'] -> backports.functools_lru_cache
ase=3.22 -> matplotlib-base -> backports.functools_lru_cache
pymatgen=2020.12.31 -> matplotlib-base[version='>=1.5'] -> backports.functools_lru_cache
matplotlib -> backports.functools_lru_cache
pylint -> backports.functools_lru_cache

Package dataclasses conflicts for:
pytorch-lightning=1.3.8 -> pytorch[version='>=1.4'] -> dataclasses
pymatgen=2020.12.31 -> dataclasses[version='>=0.6']
pyg=2.0.1 -> pytorch=1.9 -> dataclasses
matminer=0.7.3 -> pymatgen[version='>=2019.10.2'] -> dataclasses[version='>=0.6']
jupyterlab -> jupyter_server[version='>=1.4,<2'] -> dataclasses
pytorch=1.8.1 -> dataclasses

Package qt conflicts for:
pymatgen=2020.12.31 -> vtk[version='>=6.0.0'] -> qt[version='>=5.12.9,<5.13.0a0']
matplotlib -> pyqt -> qt[version='4.8.|5.6.|5.9.*|>=5.12.5,<5.13.0a0|>=5.12.9,<5.13.0a0|>=5.9.7,<5.10.0a0|>=5.6.2,<5.7.0a0|>=5.9.6,<5.10.0a0|>=5.9.4,<5.10.0a0']

Package pymatgen conflicts for:
pymatgen=2020.12.31
matminer=0.7.3 -> pymatgen[version='>=2019.10.2']

Package tomlkit conflicts for:
jupyterlab -> jupyter-packaging[version='>=0.7,<1'] -> tomlkit
pylint -> tomlkit[version='>=0.10.1']

Package jupyter_core conflicts for:
jupyterlab -> jupyter_core
jupyterlab -> jupyter_server[version='>=1.16,<2'] -> jupyter_core[version='>=4.4.0|>=4.6.0|>=4.7|>=4.7.0|>=4.6.1']

Package openblas conflicts for:
pyg=2.0.1 -> numpy -> openblas[version='0.2.20|0.2.20.|>=0.2.20,<0.2.21.0a0|>=0.3.3,<0.3.4.0a0']
ase=3.22 -> numpy -> openblas[version='0.2.20|0.2.20.|>=0.2.20,<0.2.21.0a0|>=0.3.3,<0.3.4.0a0']
seaborn -> numpy[version='>=1.13.3'] -> openblas[version='0.2.20|0.2.20.|>=0.2.20,<0.2.21.0a0|>=0.3.3,<0.3.4.0a0']
nglview -> numpy -> openblas[version='0.2.20|0.2.20.|>=0.2.20,<0.2.21.0a0|>=0.3.3,<0.3.4.0a0']
matplotlib -> numpy[version='>=1.14.6,<2.0a0'] -> openblas[version='0.2.20|0.2.20.*|>=0.2.20,<0.2.21.0a0|>=0.3.3,<0.3.4.0a0']

Package pyreadline conflicts for:
ipywidgets -> ipython[version='>=4.0.0'] -> pyreadline
jupyterlab -> ipython -> pyreadline
pylint -> dill[version='>=0.2'] -> pyreadline[version='>=1.7.1']

Package pyparsing conflicts for:
pymatgen=2020.12.31 -> matplotlib-base[version='>=1.5'] -> pyparsing[version='>=2.0.3,!=2.0.4,!=2.1.2,!=2.1.6|>=2.2.1']
jupyterlab -> packaging -> pyparsing[version='<3,>=2.0.2|>=2.0.2,!=3.0.5|>=2.0.2,<3|>=2.0.2']
ase=3.22 -> matplotlib-base -> pyparsing[version='>=2.0.3,!=2.0.4,!=2.1.2,!=2.1.6|>=2.2.1']
pip -> packaging -> pyparsing[version='<3,>=2.0.2|>=2.0.2,!=3.0.5|>=2.0.2,<3|>=2.0.2']
pyg=2.0.1 -> pyparsing
pytorch-lightning=1.3.8 -> packaging -> pyparsing[version='<3,>=2.0.2|>=2.0.2,!=3.0.5|>=2.0.2,<3|>=2.0.2']
matplotlib -> matplotlib-base[version='>=3.5.2,<3.5.3.0a0'] -> pyparsing[version='>=2.0.3,!=2.0.4,!=2.1.2,!=2.1.6|>=2.2.1']
matplotlib -> pyparsing
matminer=0.7.3 -> httplib2[version='>=0.10.3'] -> pyparsing[version='>=2.4.2,<3|>=2.4.2,<4']
seaborn -> matplotlib-base[version='>=2.1.2'] -> pyparsing[version='>=2.0.3,!=2.0.4,!=2.1.2,!=2.1.6|>=2.2.1']

Package vs2008_runtime conflicts for:
seaborn -> python -> vs2008_runtime
matplotlib -> python[version='>=2.7,<2.8.0a0'] -> vs2008_runtime[version='>=9.0.30729.1,<10.0a0']
pip -> python -> vs2008_runtime
ipywidgets -> python -> vs2008_runtime
pylint -> python[version='>=2.7,<2.8.0a0'] -> vs2008_runtime
autopep8 -> python -> vs2008_runtime
pytorch=1.8.1 -> ninja -> vs2008_runtime
nglview -> python -> vs2008_runtime
tqdm -> python[version='>=2.7'] -> vs2008_runtime
jupyterlab -> python[version='>=2.7,<2.8.0a0'] -> vs2008_runtime

Package kiwisolver conflicts for:
matplotlib -> kiwisolver
matplotlib -> matplotlib-base[version='>=3.5.2,<3.5.3.0a0'] -> kiwisolver[version='>=1.0.1']

Package typing-extensions conflicts for:
pylint -> typing-extensions[version='>=3.10.0']
matminer=0.7.3 -> pymatgen[version='>=2019.10.2'] -> typing-extensions[version='>=3.7.4.3']
matplotlib -> kiwisolver -> typing-extensions
pytorch-lightning=1.3.8 -> pytorch[version='>=1.4'] -> typing-extensions
pylint -> astroid[version='>=2.11.6,<2.12.0'] -> typing-extensions[version='>=3.10|>=3.7.4']

Package scipy conflicts for:
pymatgen=2020.12.31 -> scipy[version='>=1.4.1']
seaborn -> statsmodels[version='>=0.8.0'] -> scipy[version='>=0.14|>=1.2|>=1.3']
ase=3.22 -> scipy
pymatgen=2020.12.31 -> ase[version='>=3.3'] -> scipy[version='>=1.5,!=1.6.1|>=1.8']
matminer=0.7.3 -> ase[version='>=3.14.1'] -> scipy[version='>=0.19.1|>=1.0.1|>=1.4.1|>=1.5.0|>=1.1.0']
seaborn -> scipy[version='>=0.15.2|>=1.0.1|>=1.0']
pyg=2.0.1 -> networkx[version='>=2.4'] -> scipy[version='>=0.19.1|>=1.1.0|>=1.5,!=1.6.1|>=1.8']

Package importlib_metadata conflicts for:
matminer=0.7.3 -> pint[version='>=0.8.1'] -> importlib_metadata
jupyterlab -> jupyterlab_server[version='>=2.10,<3'] -> importlib_metadata[version='>=3.6']

Package matplotlib conflicts for:
pymatgen=2020.12.31 -> ase[version='>=3.3'] -> matplotlib[version='>=3.3']
matplotlib
seaborn -> matplotlib[version='>=1.4.3|>=2.1.2|>=2.2']
matminer=0.7.3 -> ase[version='>=3.14.1'] -> matplotlib[version='>=1.5']

Package ipywidgets conflicts for:
nglview -> ipywidgets[version='7|>=7|>=5.2.2|>=5.2.2,<6']
ipywidgets

Package yaml conflicts for:
pyg=2.0.1 -> pyyaml -> yaml[version='>=0.1.7,<0.2.0a0|>=0.2.2,<0.3.0a0|>=0.2.5,<0.3.0a0']
pytorch-lightning=1.3.8 -> pyyaml[version='>=5.1,<=5.4.1'] -> yaml[version='>=0.1.7,<0.2.0a0|>=0.2.2,<0.3.0a0|>=0.2.5,<0.3.0a0']

Package ucrt conflicts for:
python=3.8 -> vs2015_runtime[version='>=14.16.27033'] -> ucrt[version='>=10.0.20348.0']
matplotlib -> vs2015_runtime[version='>=14.16.27012,<15.0a0'] -> ucrt[version='>=10.0.20348.0']
pymatgen=2020.12.31 -> vs2015_runtime[version='>=14.16.27012'] -> ucrt[version='>=10.0.20348.0']

Package threadpoolctl conflicts for:
pyg=2.0.1 -> scikit-learn -> threadpoolctl[version='>=2.0.0']
matminer=0.7.3 -> scikit-learn[version='>=0.21.3'] -> threadpoolctl[version='>=2.0.0']

Package tbb conflicts for:
pytorch=1.8.1 -> mkl[version='>=2018'] -> tbb=2021
pymatgen=2020.12.31 -> vtk[version='>=6.0.0'] -> tbb[version='<2021.0.0a0|>=2020.2|>=2020.2,<2021.0.0a0|>=2021.4.0|>=2021.5.0|>=2019.9|>=2019.7|>=2019.6|>=2019.5|>=2019.4|>=2019.3|>=2019.0|>=2019.1']

Package future conflicts for:
pytorch-lightning=1.3.8 -> future
matminer=0.7.3 -> future[version='>=0.16.0']
pymatgen=2020.12.31 -> uncertainties -> future

Package werkzeug conflicts for:
pytorch-lightning=1.3.8 -> tensorboard[version='>=2.2.0,!=2.5.0'] -> werkzeug[version='>=0.11.15|>=1.0.1']
ase=3.22 -> flask -> werkzeug[version='>=0.14|>=0.15|>=0.15,<2.0|>=2.0|>=0.7|>=0.7,<1.0.0']

Package pytorch-mutex conflicts for:
pyg=2.0.1 -> cpuonly -> pytorch-mutex==1.0=cpu
cpuonly -> pytorch-mutex==1.0=cpu
pytorch-lightning=1.3.8 -> pytorch[version='>=1.4'] -> pytorch-mutex==1.0[build='cpu|cuda']

Package pytorch conflicts for:
pytorch-lightning=1.3.8 -> torchmetrics[version='>=0.2.0'] -> pytorch[version='>=1.3|>=1.3.1']
pytorch-lightning=1.3.8 -> pytorch[version='>=1.4']
pytorch=1.8.1

Package notebook conflicts for:
jupyterlab -> nbclassic[version='>=0.2,<1'] -> notebook[version='<7|>=4.2.0']
jupyterlab -> notebook[version='>=4.2|>=4.3|>=4.3.1']

Package libpng conflicts for:
matplotlib -> libpng[version='>=1.6.23,<1.7|>=1.6.37,<1.7.0a0|>=1.6.36,<1.7.0a0|>=1.6.35,<1.7.0a0|>=1.6.34,<1.7.0a0|>=1.6.32,<1.7.0a0']
matplotlib -> freetype=2.6 -> libpng[version='1.6.*|>=1.6.21,<1.7|>=1.6.32,<1.6.35']

Package numpy conflicts for:
seaborn -> numpy[version='>=1.13.3|>=1.9.3|>=1.15']
seaborn -> statsmodels[version='>=0.8.0'] -> numpy[version='1.10.|1.11.|1.12.|1.13.|>=1.11.|>=1.11.3,<2.0a0|>=1.11|>=1.14.6,<2.0a0|>=1.15.4,<2.0a0|>=1.16.5,<2.0a0|>=1.16.6,<2.0a0|>=1.18.5,<2.0a0|>=1.19.5,<2.0a0|>=1.21.5,<2.0a0|>=1.21.4,<2.0a0|>=1.19.2,<2.0a0|>=1.18.1,<2.0a0|>=1.17.5,<2.0a0|>=1.21.2,<2.0a0|>=1.17.0,<2.0a0|>=1.19.1,<2.0a0|>=1.17|>=1.21.6,<2.0a0|>=1.19.4,<2.0a0|>=1.12.1,<2.0a0|>=1.9.|>=1.20.3,<2.0a0|>=1.20.2,<2.0a0|>=1.13.3,<2.0a0|>=1.15.1,<2.0a0|>=1.9|>=1.8|>=1.7|>=1.11.3,<1.12.0a0|>=1.4.0']The following specifications were found to be incompatible with your system:

feature:/win-64::__win==0=0
feature:|@/win-64::__win==0=0
ipywidgets -> ipykernel[version='>=4.5.1'] -> __win
nglview -> ipykernel -> __win

Your installed version is: 0`

training project

python cdvae/run.py data=perov expname=perov model.predict_property=True

Traceback (most recent call last):
File "cdvae/run.py", line 19, in
from cdvae.common.utils import log_hyperparameters, PROJECT_ROOT
File "/home/liujinde/CSG/cdvae/cdvae/common/utils.py", line 92, in
PROJECT_ROOT: Path = Path(get_env("PROJECT_ROOT"))
File "/home/liujinde/CSG/cdvae/cdvae/common/utils.py", line 22, in get_env
raise KeyError(
KeyError: 'PROJECT_ROOT not defined and no default value is present!'

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