Code for automated fitting of machine learned interatomic potentials.
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.
Tools for machine learnt interatomic potentials
ML Performance and Extrapolation Guide
machine learning interatomic potentials aiida plugin
Using high-throughput DFT, Wannier90, and TB2J, this project calculates the magnetic exchange interactions of 2D CrI3 under mechanical strain to reveal how structural distortions modulate its Heisenberg parameters.
Collection of scripts for running phonon calculations using machine-learned interatomic potentials
Simulation of Advanced Materials (SAM) Lab at the University of Cambridge
Cartesian Natural Tensor Networks -- CarNet
Useful for: Accelerating computational materials workflows using your choice of MLIP (many accounted for). Includes the acceleration of NEB, Phonon and Bandpath calculation, AND Model benchmarking on all processes.
HH130 Database Process to Graphs
TensorNet MLIP Training on QM9 Dataset
MLIP NequIP on the MD17 Dataset
LEIGNN MLIP Training on ISO17 Dataset
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