danielathk Goto Github PK

100-days-of-ml-code

100 Days of ML Coding

100-days-of-ml-code-chinese

100-Days-Of-ML-Code中文版

aml

A... M... L...

asap

ASAP is a package that can quickly analyze and visualize datasets of crystal or molecular structures.

awesome-auto-graph-learning

A paper collection about automated graph learning

awesome-equivariant-network

Paper list for equivariant neural network

awesome-graph-transformer

Papers about graph transformers.

awesome-normalizing-flows

Awesome resources on normalizing flows.

bayesianoptimization

A Python implementation of global optimization with gaussian processes.

cdvae

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

cgcnn

Crystal graph convolutional neural networks for predicting material properties.

chainer

A flexible framework of neural networks for deep learning

chgnet

Pretrained universal neural network potential for charge-informed atomistic modeling

computationalphysics2

Advanced course in Computational Physics, see texbook at http://compphysics.github.io/ComputationalPhysics2/doc/LectureNotes/_build/html/ with an emphasis on computational quantum mechanics, machine learning and quantum computing.

d2l-en

Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 400 universities from 60 countries including Stanford, MIT, Harvard, and Cambridge.

d4ft

A JAX library for Density Functional Theory.

data-driven-first-principles-methods-for-the-study-and-design-of-alkali-superionic-conductors

Jupyter notebooks and data for our Chemistry of Materials article "Data-driven First Principles Methods for the Study and Design of Alkali Superionic Conductors"

databook_python

IPython notebooks with demo code intended as a companion to the book "Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control" by Steven L. Brunton and J. Nathan Kutz

deep-learning-topics

deeplearning

深度学习、强化学习、模仿学习与机器人

deepmd-kit

A deep learning package for many-body potential energy representation and molecular dynamics

deepmind-research

This repository contains implementations and illustrative code to accompany DeepMind publications

deepqmc

Deep learning quantum Monte Carlo for electrons in real space

deepsolid

A library combining solid quantum Monte Carlo and neural network.

deeptime

Python library for analysis of time series data including dimensionality reduction, clustering, and Markov model estimation

dffs

dft_pib_code

Interactive Jupyter Notebooks for learning the fundamentals of Density-Functional Theory (DFT)

dftatom

Routines for Radial Integration of Dirac, Schrödinger, and Poisson Equations

dftworks

dl4csrc

Hitchhiker's Guide to Deep Learning for Computational Scientists