Find open-source science resources

A package for creating fast and accurate interatomic potentials.

Tool to build force field input files for molecular simulation.

Convert AMBER forcefields from ANTECHAMBER to GROMACS format.

An open, extensible Python framework for GPU-accelerated alchemical free energy calculations.

A module for solving and visualizing the Schrödinger equation.

Usage-Instructions) - A program to visualize reaction networks.

A quantum chemistry package written in Python.

A calculator incorporating various empirical pair and many-body potentials.

Psi4-based reference implementations and Jupyter notebook-based tutorials for foundational quantum chemistry methods.

Interaction Fingerprints for protein-ligand complexes and more.

A Simulation Tool for Fractured and Deformable Porous Media.

Python Library for Automating Molecular Simulation: input preparation, job execution, file management, output processing and building data workflows.

A library for estimating thermochemical properties of molecules and adsorbates using group additivity.

Parameter/topology editor and molecular simulator with visualization capability.

A library and command-line tool for building and analyzing complex homogeneous microkinetic models from quantum chemistry calculations, with support for quasi-harmonic thermochemistry, quantum tunnelling corrections, molecular symmetries and more.

A batteries-included toolkit for the GPU-accelerated OpenMM molecular simulation engine.

Open source PEM (Proton Exchange Membrane) fuel cell simulation tool.

Open Drug Discovery Toolkit, a modular and comprehensive toolkit for use in cheminformatics, molecular modeling etc.

File parser/converter for QM, MD and plane-wave DFT programs.

High-Throughput Molecular Dynamics: Programming Environment for Molecular Discovery.

The "FRamewOrk for Molecular AGgregate Excitations" enables localised QM/QM' excited state calculations in a solid state environment.

A package to 'build' collections of materials properties from the output of computational materials calculations.

A library for computational chemistry (DFT) for input file generation, data extraction, method screening and analysis.

A tool and library for creating quantum chemistry input files.

General purpose tools for high-throughput catalysis.

A collection of object-oriented software tools for problems involving chemical kinetics, thermodynamics, and transport processes.

A library containing basis sets for use in quantum chemistry calculations. In addition, this library has functionality for manipulation of basis set data.

atomate2 is a library of computational materials science workflows.

Makes alchemical free energy calculations easier by leveraging the full power and flexibility of the PyData stack.

A benchmarking platform for molecular generation models.

A package for benchmarking of models for _de novo_ molecular design.

A platform for graph-based molecular generation using graph neural networks.

Library with several compositional and structural material descriptors, along with a few pre-trained neural network models of material properties.

Self-Referencing Embedded Strings (SELFIES): A 100% robust molecular string representation.

Ensemble of automated machine learning protocols that can be run sequentially through a single command line. The program works for regression and classification problems.

Library for fast calculations of **mo**lecula**r** **fe**at**u**re**s** from 3D structures for machine learning with a focus on steric descriptors.

Aims to provide useful high-level interfaces that make ML for materials science as easy as possible.

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

a robust molecular representation learning framework against distribution shifts.

Library of descriptors to aid in the data-mining of materials properties, created by the Lawrence Berkeley National Laboratory.

Descriptor library containing a variety of fingerprinting techniques, including the Smooth Overlap of Atomic Positions (SOAP).

A Deep Learning Library for Compound and Protein Modeling DTI, Drug Property, PPI, DDI, Protein Function Prediction.

A deep learning framework (based on Chainer) with applications in Biology and Chemistry.

Enables machine learning on three-dimensional molecular structure.

A library for processing, analyzing and modeling spectroscopic data.

A toolkit for visualizations in materials informatics.

Predicts the pKa values of ionizable groups in proteins and protein-ligand complexes based in the 3D structure.

This package provides a periodic table of the elements with support for mass, density and xray/neutron scattering information.

Calculate mass, elemental composition, and mass distribution spectrum of a molecule given by its chemical formula, relative element weights, or sequence.

A package for working with nuclear magnetic resonance (NMR) data including functions for reading common binary file formats and processing NMR data.