Message Passing Neural Networks for Molecule Property Prediction

Python module for quantum chemistry

Robust representation of semantically constrained graphs, in particular for molecules in chemistry

Graph neural networks for molecular design.

The Open Forcefield Toolkit provides implementations of the SMIRNOFF format, parameterization engine, and other tools. Documentation available at http://open-forcefield-toolkit.readthedocs.io

Descriptor computation(chemistry) and (optional) storage for machine learning

MatDeepLearn, package for graph neural networks in materials chemistry

Crystal Toolkit is a framework for building web apps for materials science and is currently powering the new Materials Project website.

Tool to build force field input files for molecular simulation

A python package for exploring end-to-end chemistry workflows on quantum computers and simulators.

Code to support the paper: A. Fabrizio, A. Grisafi, B. Meyer, M. Ceriotti, and C. Corminboeuf, “Electron density learning of non-covalent systems”, Chem. Sci. 10, 9492 (2019)