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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 …
Working collection of papers, repos and models of transformer based language models trained or tuned for the Chemical domain, from natural language to chemical modeling and property prediction
Generation of molecular crystal structures and robust workflows for polymorph prediction
This repository contains a collection of resources and papers on GNN Models on Crystal Solid State Materials
The papers and code related to deep generative models for material discovery.
Materials science with Python at the atomic-scale
SevenNet - a graph neural network interatomic potential package supporting efficient multi-GPU parallel molecular dynamics simulations.
DFTB+ general package for performing fast atomistic simulations
An evaluation framework for machine learning models simulating high-throughput materials discovery.
This repository contains the official PyTorch implementation of MatRIS.
pytorch implementation of dftd2 & dftd3 (not actively maintained)
Monte Carlo simulation for classical spin system implemented by metropolics/wolff_cluster... algorithm
Open Babel is a chemical toolbox designed to speak the many languages of chemical data.
Pretrained universal neural network potential for charge-informed atomistic modeling https://chgnet.lbl.gov
MACE - Fast and accurate machine learning interatomic potentials with higher order equivariant message passing.
A modular framework for neural networks with Euclidean symmetry
Graph Neural Network Library for PyTorch
🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.
Code for loralib, an implementation of "LoRA: Low-Rank Adaptation of Large Language Models"
TorchANI 2.0 is an open-source library that supports training, development, and research of ANI-style neural network interatomic potentials. It was originally developed and is currently maintained …