computational-materials-science

🏆 A ranked list of awesome atomistic machine learning projects ⚛️🧬💎.

MatterSim: A deep learning atomistic model across elements, temperatures and pressures.

Pretrained universal neural network potential for charge-informed atomistic modeling https://chgnet.lbl.gov

quacc is a flexible platform for computational materials science and quantum chemistry that is built for the big data era.

doped is a Python software for the generation, pre-/post-processing and analysis of defect supercell calculations, implementing the defect simulation workflow in an efficient, reproducible, user-friendly yet powerful and fully-customisable manner.

A collection of interactive notebooks to explain concepts of quantum mechanics and related topics

Package to perform automatic bonding analysis with the program Lobster in the field of computational materials science and quantum chemistry

A Virtual Machine for computational materials science

Cellular automata code for alloy nucleation and solidification written with Kokkos

Application of Large Language Models (LLM) for computational materials science - visit jan-janssen.com/LangSim

Band structure unfolding made easy!

A tool for streamlining data analysis and visualisation for thermoelectrics and charge carrier transport in computational materials science.

A workflow to create computation-ready metal-organic framework database.

Peridynamics with the Cabana library

Grand canonical optimization of grain boundary phases.

Original implementation of CSPML

Phase-field code in MATLAB to solve the phase-field model of Fan & Chen for Grain Growth phenomena in 2D

Python python toolset for Structure-Informed Property and Feature Engineering with Neural Networks. It offers unique advantages through (1) effortless extensibility, (2) optimizations for ordered, dilute, and random atomic configurations, and (3) automated model tuning.

PINNs-MPF is a comprehensive framework designed for simulating interface dynamics using Physics-Informed Neural Networks (PINNs). Leveraging machine learning techniques, this framework offers an efficient implementation for the multi-phase-field model

A free and open platform for interactive benchmarking and simulations using machine learning interatomic potentials