computational-materials-science 开源项目推荐（152 个）

JuDFTteam /best-of-atomistic-machine-learning

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

microsoft /mattersim

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

CederGroupHub /chgnet

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

Quantum-Accelerators /quacc

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

SMTG-Bham /doped

Doped 是一款用于生成、预处理和分析缺陷超级单元计算的 Python 软件，以高效、可复现、用户友好且功能强大且完全可定制的方式实现缺陷模拟工作流程。

osscar-org /quantum-mechanics

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

DigiMatChem /LobsterPy

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

marvel-nccr /quantum-mobile

A Virtual Machine for computational materials science

llnl /ExaCA

Cellular automata code for alloy nucleation and solidification written with Kokkos

jan-janssen /LangSim

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

SMTG-Bham /easyunfold

Band structure unfolding made easy!

SMTG-Bham /ThermoParser

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

Chung-Research-Group /CoRE-MOF-Tools

创建可供计算的金属有机框架数据库的工作流程。

ORNL /CabanaPD

Peridynamics with the Cabana library

enze-chen /grip

Grand canonical optimization of grain boundary phases.

Minoru938 /CSPML

Original implementation of CSPML

abhinavroy1999 /grain-growth-phase-field-code

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

SFETNI /PINNs_MPF--a-Physics-Informed-Neural-Network-for-Multi-Phase-Field-problems

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

mlipstudio /MLIP-Studio

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

PhasesResearchLab /pySIPFENN

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.