materials-science

Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology

A deep learning package for many-body potential energy representation and molecular dynamics

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

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

FiPy is a Finite Volume PDE solver written in Python

Data mining for materials science

NequIP is a code for building E(3)-equivariant interatomic potentials

Density-functional toolkit

DScribe is a python package for creating machine learning descriptors for atomistic systems.

Cross platform, open source application for the processing, visualization, and analysis of 3D tomography data

Curated list of known efforts in materials informatics

Python for Materials Machine Learning, Materials Descriptors, Machine Learning Force Fields, Deep Learning, etc.

Scientific analysis of nanoscale materials imaging data

CALPHAD tools for designing thermodynamic models, calculating phase diagrams and investigating phase equilibria.

A Python library which allows construction and manipulation of complex molecules, as well as automatic molecular design and the creation of molecular databases.

Heavyweight plotting tools for ab initio calculations

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

Atomsk: A Tool For Manipulating And Converting Atomic Data Files -

Data Analysis program and framework for materials science data analytics, based on the managing framework SIMPL framework.

Deep and Machine Learning for Microscopy