The mlearn package is a benchmark suite for machine learning interatomic potentials for materials science. It enables a seamless way to develop various potentials and provides LAMMPS-driven properties predictor with developed potentials as plugins.
The usage of mlearn requires installation of specific packages and the plugins in LAMMPS. Please see detailed installation instructions for all descriptors.
- Gaussian Approximation Potential (GAP)
- Moment Tensor Potential (MTP)
- Neural Network Potential (NNP)
- Linear Spectral Neighbor Analysis Potential (SNAP) and quadratic SNAP
- Gaussian Approximation Potential: Bartók, A. P.; Payne, M. C.; Kondor, R.; Csányi, G. Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons. Physical Review Letters 2010, 104, 136403. doi:10.1103/PhysRevLett.104.136403.
- Moment Tensor Potential: Shapeev, A. V. Moment tensor potentials: A class of systematically improvable interatomic potentials. Multiscale Modeling & Simulation, 14(3), 1153-1173. doi:10.1137/15M1054183
- Neural Network Potential: Behler, J., & Parrinello, M. Generalized neural-network representation of high-dimensional potential-energy surfaces. Physical Review Letters 2007, 98, 146401. doi:10.1103/PhysRevLett.98.146401
- Spectral Neighbor Analysis Potential: Thompson, A. P., Swiler, L. P., Trott, C. R., Foiles, S. M., & Tucker, G. J. Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials. Journal of Computational Physics, 285, 316-330. doi:10.1016/j.jcp.12.018