chao-research / xca

Crystallography Companion Agent

Crystallography Companion Agent (xca)

Overview

XCA is a psuedo-unsupervised learning approach for handling spherically integrated (powder) X-ray diffraction data. The approach depends on accurate data synthesis that encompasses the multitude of abberations which can impact a diffraction pattern. The magnitude of influence of these aberrations is meant to be informed by experience given an experimental design (e.g. well prepared powders will experience significantly less texturing than epitaxially grown thin films.) The dataset synthesis is accomplished using the cctbx, starting from .cif files of potential phases. From synthetic datasets an ensemble of feed forward convolutional neural networks can be trained, and subsequently used to predict phase existence in experimental data.

System requirements

Hardware requirements

xca package requires only a standalone computer with enough RAM to support the in-memory operations. For advanced use, a CUDA enabled GPU is recommended.

Software requirements

OS requirements

This package is supported for macOS and Linux. The package has been tested on the following systems:

• macOS: Catalina (10.15.7)
• Linux: Ubuntu 18.04

Python dependencies

xca Dataset generation makes extensive use of the cctbx, which is currently best installed into a conda environment.

The machine learning depends on a scientific tensorflow stack:

tensorflow >= 2.1.0
# tensorflow-gpu will be installed if a gpu is available 
numpy
scikit-learn
scipy

Installation guide

Due to the current unavailability of the cctbx on PyPi channels, we recommend first setting up a conda environment for the cctbx. The remaining dependencies can be installed via pip.

conda create -n xca -c conda-forge cctbx-base python=3.7
conda activate xca
git clone https://github.com/maffettone/xca
cd xca
python -m pip install .

Getting started

A simple demonstration

A simple example of the full training pipeline is demonstrated in the simple_example.py script. Executing this will do the following in a tmp directory:

1. Synthesize 100 example patterns for each phase of the three experimental systems presented in the paper below.
2. Convert those patterns into a tfrecords object.
3. Train an ensemble model, print the results, and save the full model.

cd xca/examples/arxiv200800283
python simple_example.py

This will take a few minutes to run for each example, approximately 10 minutes in total. The output will print the path to each cif file as it generates the relevant set of synthetic data. This includes 4 files for BaTiO, 5 files for ADTA, and 31 files for Ni-Co-Al. The output will then print the ensemble model summary. The time for each epoch will be displayed on with 8 epochs for each model training. Lastly results dictionary will be sloppily printed to show the loss, training, and validation metrics.

Details of generic synthesis and training can be found in example_synthesis.py and example_training.py.

Literature details

The application of this package is demonstrated in aXiv:2008.00283. To reproduce the models presented in this paper, the dataset synthesis should be scaled (use of multiprocessing is encouraged) to produce 50,000 patterns per phase using the same parameterization presented in example_synthesis.py.

ABSTRACT: The discovery of new structural and functional materials is driven by phase identification, often using X-ray diffraction (XRD). Automation has accelerated the rate of XRD measurements, greatly outpacing XRD analysis techniques that remain manual, time consuming, error prone, and impossible to scale. With the advent of autonomous robotic scientists or self-driving labs, contemporary techniques prohibit the integration of XRD. Here, we describe a computer program for the autonomous characterization of XRD data, driven by artificial intelligence (AI), for the discovery of new materials. Starting from structural databases, we train an ensemble model using a physically accurate synthetic dataset, which output probabilistic classifications --- rather than absolutes --- to overcome the overconfidence in traditional neural networks. This AI agent behaves as a companion to the researcher, improving accuracy and offering unprecedented time savings, and is demonstrated on a diverse set of organic and inorganic materials challenges. This innovation is directly applicable to inverse design approaches, robotic discovery systems, and can be immediately considered for other forms of characterization such as spectroscopy and the pair distribution function.