yugang-hello's Stars
choderalab/sake
Spatial Attention Kinetic Networks with E(n)-Equivariance
mir-group/phoebe
A high-performance framework for solving phonon and electron Boltzmann equations
stefanch/sGDML
sGDML - Reference implementation of the Symmetric Gradient Domain Machine Learning model
OUnke/SpookyNet
Reference implementation of "SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects"
deepmodeling/Uni-Mol
Official Repository for the Uni-Mol Series Methods
Namkyeong/NoisyNodes_Pytorch
Pytorch implementation of "Very Deep Graph Neural Networks via Noise Regularisation"
deepmodeling/dpgen
The deep potential generator to generate a deep-learning based model of interatomic potential energy and force field
openai/point-e
Point cloud diffusion for 3D model synthesis
f/awesome-chatgpt-prompts
This repo includes ChatGPT prompt curation to use ChatGPT better.
m-k-S/awesome-machine-learning-atomistic-simulation
An overview of literature that discusses the use of machine learning for atomistic simulations
davkovacs/BOTNet-datasets
This repo contains the datasets used for the paper The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentials.
ICAMS/python-ace
jmgoff/sym_ACE_lite
Modules and libraries to generate permutation-adapted atomic cluster expansion (ACE) descriptors. The libraries may be used to generate descriptor labels as well as generalized wigner symbols, used to reduce products of spherical harmonics. There are tools to write potential files compatible with the ML-PACE package in LAMMPS.
ACEsuit/ACE.jl
Parameterisation of Equivariant Properties of Particle Systems
kyonofx/MDsim
[TMLR 2023] Training and simulating MD with ML force fields
TUM-DAML/gemnet_pytorch
GemNet model in PyTorch, as proposed in "GemNet: Universal Directional Graph Neural Networks for Molecules" (NeurIPS 2021)
risilab/cormorant
Codebase for Cormorant Neural Networks
YKQ98/Matformer
Official code for Periodic Graph Transformers for Crystal Material Property Prediction (NeurIPS 2022)
dmlc/dgl
Python package built to ease deep learning on graph, on top of existing DL frameworks.
lsj2408/Transformer-M
[ICLR 2023] One Transformer Can Understand Both 2D & 3D Molecular Data (official implementation)
lucidrains/egnn-pytorch
Implementation of E(n)-Equivariant Graph Neural Networks, in Pytorch
ACEsuit/mace
MACE - Fast and accurate machine learning interatomic potentials with higher order equivariant message passing.
pyscal/pyscal
Python library written in C++ for calculation of local atomic structural environment
materialsproject/pymatgen
Python Materials Genomics (pymatgen) is a robust materials analysis code that defines classes for structures and molecules with support for many electronic structure codes. It powers the Materials Project.
RomanovIgnat/MEGNet_PyTorch
mir-group/flare
An open-source Python package for creating fast and accurate interatomic potentials.
speediedan/finetuning-scheduler
A PyTorch Lightning extension that accelerates and enhances foundation model experimentation with flexible fine-tuning schedules.
pyg-team/pytorch_geometric
Graph Neural Network Library for PyTorch
PhasesResearchLab/pySIPFENN
Python python toolset for Structure-Informed Property and Feature Engineering with Neural Networks. It offers unique advantages through (1) effortless extensibility, (2) optimizations for ordered, dilute, and random atomic configurations, and (3) automated model tuning.
lasso-net/lassonet
Feature selection in neural networks