Machine-Learned Interatomic Potential eXploration (mlipx) is designed at BASF for evaluating machine-learned interatomic potentials (MLIPs). It offers a growing set of evaluation methods alongside powerful visualization and comparison tools.

A Python package for processing molecules with RDKit in scikit-learn

Formula parser and evaluator for Wilkinson Notation and dataframes arithmetics

Chemical intuition for surface science in a package.

Modular standalone fat-client GUI for X-ray diffraction data simulations, analysis, and sharing

UI-kit based on *spectre.css* and powered by *svelte*

The home of mlipx recipes and result data that can be updated by community members with new models and testing protocols. This repo can then be used with remote zndraw portal for quick visualization and analysis.

Lightweight tools for experimental design and multi-objective optimization.

Functions for automated statistical analysis of Xenopus Eleutherembryonic Thyroid Assays (XETA), Rapid Androgen Disruption Activity Reporter (RADAR) assays and Rapid Estrogen Activity In Vitro (REACTIV) assays.