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mdcs-api-tools

megnet

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

melting_points_ml

Data analytics for molecular solids melting points

minimal-mpnn

A minimal message-passing neural network for molecular properties

ml-quaternary-heuslers

Using machine learning to predict new quaternary Heusler materials

modular-data-models-include

molecular-dqn

A minimal example for running molecular DQNs

nfp

oqmd-dl-analysis

Analysis of the predictions made by a deep learning model trained on the OQMD

parsl

Parsl - Parallel Scripting Library

pif-dft

Tools for converting from DFT codes into PIF objects

pipeline_prototype

Pipeline prototyping

play-by-post-helper

A Bot to assist playing RPGs on Discord

proxystore

Python Lazy Object Proxy Interface for Distributed Stores

pymatgen

Python Materials Genomics (pymatgen) is a robust materials analysis code that defines core object representations for structures and molecules with support for many electronic structure codes. It is currently the core analysis code powering the Materials Project.

pypif

Python toolkit for working with PIFs

python-ternary

:small_red_triangle: Ternary plotting library for python with matplotlib

pyvoro

2D and 3D Voronoi tessellations: a python entry point for the voro++ library

qball

Qball (also known as qb@ll) is a first-principles molecular dynamics code that is used to compute the electronic structure of atoms, molecules, solids, and liquids within the Density Functional Theory (DFT) formalism. It is a fork of the Qbox code by Francois Gygi.

qcengine

Quantum chemistry program executor and IO standardizer (QCSchema).

qml

QML: Quantum Machine Learning

qmpy

A suite of computational materials science tools.

schnetpack

SchNetPack - Deep Neural Networks for Atomistic Systems

scikit-learn

scikit-learn: machine learning in Python

ternary-semiconductors-mhm

Scripts from a paper on discovering ternary semiconductors with machine learning and crystal structure prediction

toolbox

Toolbox is a collection of the Materials Data Facility tools and utilities.

ward-npj-2016-examples

Scripts for replicating a paper on predicting the properties of materials with machine learning