Materials Tools

Software libraries for materials science and atomistic simulations.

Core Materials Stack

Tool	Description	Link
pymatgen	Python Materials Genomics — analysis and manipulation	pymatgen.org
matminer	Data mining and ML for materials	hackingmaterials.lbl.gov/matminer
ASE	Atomic Simulation Environment	wiki.fysik.dtu.dk/ase
JARVIS-tools	Integrated workflows for materials	github
pyiron	Integrated development environment for computational materials science	pyiron.org
atomate2	Library of computational materials science workflows	github
emmet	Build collections of materials properties	github
CatKit	High-throughput catalysis tools	github

High-Throughput Frameworks

Tool	Description	Link
AFLOW	High-Throughput ab-initio Computing framework (C++)	materials.duke.edu
AiiDA	Automated Infrastructure and Database for Ab-initio design	aiida.net
atomate	Materials science workflows built on FireWorks	hackingmaterials
quacc	Python platform for high-throughput computational materials science	github
SEAMM	Simulation Environment for Atomistic and Molecular Modeling	molssi-seamm
tilde	Python framework for ab initio data repositories	github

Cloud Simulation Platforms

Platform	Description	Link
Mat3ra	Materials Modeling 2.0 cloud engine	mat3ra.com
AiiDAlab	Web platform and GUI for AiiDA in the cloud	materialscloud.org
Materials Square	Ab initio and CALPHAD simulations cloud	materialsquare.com
Matlantis	Accelerated materials discovery platform	matlantis.com
Azure Quantum Elements	Microsoft platform with generative chemistry	quantum.microsoft.com

Machine Learning for Materials

Tool	Description	Link
MAML	High-level interfaces for materials science ML	github
DScribe	Descriptor library with various fingerprinting techniques	github
MEGNet	Graph networks for molecules and crystals	github
CGCNN	Crystal graph networks for material properties	github
Matminer	Library of descriptors for data-mining materials properties	github
XenonPy	Material descriptors and neural network models	github
matbench-discovery	Benchmark for ML-guided materials discovery	github
amp	Machine-learning for atomistic calculations	docs

Neural Network Potentials

Tool	Description	Link
MACE	Message passing neural network potentials	github
NequIP	E(3)-equivariant neural network potentials	github
SchNetPack	Deep learning for molecules and materials	github
CHGNet	Neural network potential for atomistic modeling	github
FLARE	Creating fast and accurate interatomic potentials	github
FitSNAP	Training SNAP interatomic potentials	github
NeuralForceField	PyTorch-based neural network force field	github

Atomistic Simulations

Tool	Description	Link
phonopy	Phonon calculations at harmonic levels	docs
GPAW	Density-functional theory Python code	docs
QUIP	Software tools for molecular dynamics simulations	docs
pysic	Empirical pair and many-body potentials	github
tsase	Transition state calculations library	docs
symmetry	Materials symmetry analysis library	docs
cctbx	Computational Crystallography Toolbox	docs
fromage	Framework for Molecular Aggregate Excitations	github
PorePy	Fractured porous media simulation	github

Visualization

Tool	Description	Link
VESTA	3D crystal structure visualization	jp-minerals.org/vesta
py3Dmol	3D visualization in Jupyter	github
pymatviz	Visualizations in materials informatics	github
sumo	Plotting and analysis of ab initio data	docs
surfinpy	Surface calculation data visualization	docs
chemiscope	Interactive structure/property explorer	github
pymoldyn	Atomic clusters and materials viewer	docs

Database & Analysis Tools

Tool	Description	Link
JARVIS	Automated materials discovery repository	jarvis.nist.gov
mathchem	Topological indices of molecular graphs	docs
LModeA-nano	Chemical bond strength via local vibrational mode theory	docs

Awesome Lists

List	Focus
awesome-materials-informatics	Overview of materials informatics software
Best of Atomistic ML	510+ atomistic ML projects
Materials-Related Databases	Curated materials science databases
atomistic.software	Major atomistic simulation engines
Porous Materials AI Gym	ML datasets for porous materials

Getting Started


conda create -n materials python=3.10
conda activate materials
 
# Core stack
pip install pymatgen matminer ase
 
# For ML
pip install torch scikit-learn dscribe