We present a Kokkos-accelerated implementation of the Moment Tensor Potential (MTP) for LAMMPS, designed to improve both computational performance and portability across CPUs and GPUs. This package introduces an optimized CPU variant--achieving up to 2x speedups over existing implementations--and two new GPU variants: a thread-parallel version for large-scale simulations and a block-parallel version optimized for smaller systems. It supports three core functionalities: standard inference, configuration-mode active learning, and neighborhood-mode active learning. Benchmarks and case studies demonstrate efficient scaling to million-atom systems, substantially extending accessible length and time scales while preserving the MTP's near-quantum accuracy and native support for uncertainty quantification.
@article{arxiv.2510.00193,
title = {A Kokkos-Accelerated Moment Tensor Potential Implementation for LAMMPS},
author = {Zijian Meng and Karim Zongo and Edmanuel Torres and Christopher Maxwell and Ryan Eric Grant and Laurent Karim Béland},
journal= {arXiv preprint arXiv:2510.00193},
year = {2025}
}