materials-science

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

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.

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

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

Density-functional toolkit

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

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

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

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

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

Things that you should (and should not) do in your Materials Informatics research.

Open-source library for analyzing the results produced by ABINIT

A list of databases, datasets and books/handbooks where you can find materials properties for machine learning applications.