materialsproject/atomate2

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.

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

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

AI-driven workbench for computational materials science — interactive 3D structure viewer, natural-language CatBot assistant, visual DAG workflow engine, HPC job submission. Tauri desktop app with SvelteKit frontend and FastAPI Python backend. Drives VASP, ORCA, CP2K, QE, GPAW, DFTB+, SIESTA, LAMMPS.

Graph deep learning library for materials

ALCHEMI Toolkit-Ops is a collection of optimized batch kernels to accelerate computational chemistry and material science workflows.

Code for automated fitting of machine learned interatomic potentials.

Data mining for materials science

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.