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awesome-diffusion-models-for-science

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.

cgcnn_paddle

Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties implemented based on the Paddle framework

deep_md_test

deepmd-kit

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

diffcsp

diffcsp-pp

[ICLR 2024] The implementation for the paper "Space Group Constrained Crystal Generation"

dig

A library for graph deep learning research

dpd_rbc

graphormer

Graphormer is a general-purpose deep learning backbone for molecular modeling.

jax-md

Differentiable, Hardware Accelerated, Molecular Dynamics

kpgt

codes for KPGT (Knowledge-guided Pre-training of Graph Transformer)

lammps

Public development project of the LAMMPS MD software package

lammps-configs

LAMMPS configuration files

lammps_meso_cpu

lammps_meso_gpu

materials_discovery

openmm

OpenMM is a toolkit for molecular simulation using high performance GPU code.

paddle_deepmd-kit

paddlescience

PaddleScience is SDK and library for developing AI-driven scientific computing applications based on PaddlePaddle.

schnetpack

SchNetPack - Deep Neural Networks for Atomistic Systems

toy-md

Python code for learning Molecular Dynamics simulations

transformer-m

[ICLR 2023] One Transformer Can Understand Both 2D & 3D Molecular Data (official implementation)