machine-learning-potentials

🌟 [NeurIPS '25 Spotlight] Fair and transparent benchmark of machine learning interatomic potentials (MLIPs), beyond basic error metrics https://openreview.net/forum?id=SAT0KPA5UO

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.

This repository contains a suite of scripts designed to automate thermodynamic properties of materials, using MatterSim, a universal machine learning interatomic potential, for geometry optimization and force constant calculations, integrated with Phonopy-QHA for Quasi-Harmonic Approximation.

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

Article-specific code, derived data, frozen predictions, and tests for leakage-controlled probing of site magnetism in universal interatomic potentials

Automated phonon and thermal conductivity screening harness, featuring a 103-compound benchmark exposing systematic anharmonic bias in foundation MLIPs.

Topological witnesses for polymer chains in Rust — Gauss linking in closed form, Alexander determinant at t=-1, and the falsification harness that withdrew three of its own four hypotheses.

Turn ab initio output into geometry files for machine-learning force fields

Active-learning orchestration and automated coordinate reduction for expensive atomistic simulation campaigns.

Per-atom uncertainty quantification and active learning for MACE machine-learning interatomic potentials

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

Falsifiable research program for safe nanofabrication: matter representations, adaptive physics, matter compiler, metrology, and error correction.

Loss-aware converter for computational-chemistry file formats — reports what every conversion keeps, drops, or fabricates.

Computational investigation of defect chemistry and structural disorder in IGZO, a transparent conducting oxide and amorphous oxide semiconductor, using DFT, molecular dynamics and machine-learning potentials.

Hybrid-Quantum Dynamics Analysis: a zero-base framework for ferroelectric phase transitions in PbTiO3, coupling VASP DFT baselines with ab initio molecular dynamics and DeepMD potentials, plus finite-size scaling to extrapolate the transition temperature.

From metadynamics to machine-learning interatomic potentials — research code and notes.