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2d_perovskite_synthesizability

alignn

Atomistic Line Graph Neural Network

awesome-ai-painting

AI绘画资料合集（包含国内外可使用平台、使用教程、参数教程、部署教程、业界新闻等等） stable diffusion tutorial、disco diffusion tutorial、 AI Platform

bestpractices

Things that you should (and should not) do in your Materials Informatics research.

blog

Everything about database,business.(Most for PostgreSQL).

cgat

Crystal graph attention neural networks for materials prediction

cgcnn

Crystal graph convolutional neural networks for predicting material properties.

chemprop

Message Passing Neural Networks for Molecule Property Prediction

classical-rpp

Global optimization of Ruddlesden-Popper perovskites using a classical potential.

crystal-twins

deepfmpo

Code accompanying the paper "Deep reinforcement learning for multiparameter optimization in de novo drug design"

g-metad

Metadynamics code on the G-space.

gatgnn

Pytorch Repository for our work: Graph convolutional neural networks with global attention for improved materials property prediction

hefei-namd

ab-initio nonadiabatic molecular dynamics program

hgraph2graph

Hierarchical Generation of Molecular Graphs using Structural Motifs

icml18-jtnn

Junction Tree Variational Autoencoder for Molecular Graph Generation (ICML 2018)

interpret

Fit interpretable models. Explain blackbox machine learning.

jarvis

JARVIS-Tools: an open-source software package for data-driven atomistic materials design. Publications: https://scholar.google.com/citations?user=3w6ej94AAAAJ

m3gnet

Materials graph network with 3-body interactions featuring a DFT surrogate crystal relaxer and a state-of-the-art property predictor.

mace

MACE - Fast and accurate machine learning interatomic potentials with higher order equivariant message passing.

magpie_python

Python version of magpie attribute generators.

matdeeplearn

MatDeepLearn, package for graph neural networks in materials chemistry

megnet

Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals

ml-visuals

🎨 ML Visuals contains figures and templates which you can reuse and customize to improve your scientific writing.

moformer

Transformer model for structure-agnostic metal-organic frameworks (MOF) property prediction

nmp_qc

Our own implementation of Neural Message Passing for Computer Vision paper

paddlehelix

Bio-Computing Platform Featuring Large-Scale Representation Learning and Multi-Task Deep Learning “螺旋桨”生物计算工具集

pyband

band plot using python matplotlib

schnet

SchNet - a deep learning architecture for quantum chemistry

spin-texture-vasp

Spin Texture contains a C++-code as well as gnuplot scripts for generating and plotting spin textures