A Kokkos-Accelerated Moment Tensor Potential Implementation for LAMMPS
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866914069401829376 |
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| author | Meng, Zijian Zongo, Karim Torres, Edmanuel Maxwell, Christopher Grant, Ryan Eric Béland, Laurent Karim |
| author_facet | Meng, Zijian Zongo, Karim Torres, Edmanuel Maxwell, Christopher Grant, Ryan Eric Béland, Laurent Karim |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_00193 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Kokkos-Accelerated Moment Tensor Potential Implementation for LAMMPS Meng, Zijian Zongo, Karim Torres, Edmanuel Maxwell, Christopher Grant, Ryan Eric Béland, Laurent Karim Materials Science 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. |
| title | A Kokkos-Accelerated Moment Tensor Potential Implementation for LAMMPS |
| topic | Materials Science |
| url | https://arxiv.org/abs/2510.00193 |