A simple and fast python library to handle the data generated from molecular dynamics simulations

Reference-audited AI coding-agent skill library for computational chemistry, materials modeling, atomistic simulation, scientific ML, and related workflows.

Automatic-differentiation-based Gaussian processes for molecular and materials potential energy surfaces.

Committee-based active learning framework for building AENET interatomic potential training datasets

ML-potential reaction-pathway explorer for catalyst surfaces: reaction networks, NEB barriers, cross-model uncertainty, and publication-ready diagrams.