GPU-MetaD: Full-Life-Cycle GPU Accelerated Metadynamics with Machine Learning Potentials
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arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866911536694427648 |
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| author | Zhang, Haoting Jia, Qiuhan Zhang, Zhennan Zhu, Yijie Zhang, Zhongwei Wang, Junjie Shi, Jiuyang Fan, Zheyong Sun, Jian |
| author_facet | Zhang, Haoting Jia, Qiuhan Zhang, Zhennan Zhu, Yijie Zhang, Zhongwei Wang, Junjie Shi, Jiuyang Fan, Zheyong Sun, Jian |
| contents | Large-scale molecular dynamics simulations with high accuracy have been increasingly popular for their capability to bridge the gap between atomistic modeling and mesoscale phenomena. Both machine learning potentials and enhanced sampling approaches offer substantial improvements in high-accuracy simulation efficiency, which can be further boosted through GPU acceleration. However, an efficient framework combining these advances for extending simulations to large systems and long timescales remains elusive. In this work, we proposed a full-life-cycle GPU accelerated metadynamics simulations package GPU-MetaD. Benchmarking across molecular, interface, and bulk systems demonstrates that GPU-MetaD efficiently handles diverse atomic systems and delivers an order-of-magnitude performance improvement. Building on this demonstrated capability, it enables ab-initio-level rare-event sampling for systems comprising millions of atoms on a typical single GPU. This capability allows us to reveal a previously unknown size-dependent two-step nucleation mechanism in gallium nitride (GaN), highlighting the potential of GPU-MetaD for uncovering complex rare events in realistic large-scale materials systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_06873 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GPU-MetaD: Full-Life-Cycle GPU Accelerated Metadynamics with Machine Learning Potentials Zhang, Haoting Jia, Qiuhan Zhang, Zhennan Zhu, Yijie Zhang, Zhongwei Wang, Junjie Shi, Jiuyang Fan, Zheyong Sun, Jian Computational Physics Large-scale molecular dynamics simulations with high accuracy have been increasingly popular for their capability to bridge the gap between atomistic modeling and mesoscale phenomena. Both machine learning potentials and enhanced sampling approaches offer substantial improvements in high-accuracy simulation efficiency, which can be further boosted through GPU acceleration. However, an efficient framework combining these advances for extending simulations to large systems and long timescales remains elusive. In this work, we proposed a full-life-cycle GPU accelerated metadynamics simulations package GPU-MetaD. Benchmarking across molecular, interface, and bulk systems demonstrates that GPU-MetaD efficiently handles diverse atomic systems and delivers an order-of-magnitude performance improvement. Building on this demonstrated capability, it enables ab-initio-level rare-event sampling for systems comprising millions of atoms on a typical single GPU. This capability allows us to reveal a previously unknown size-dependent two-step nucleation mechanism in gallium nitride (GaN), highlighting the potential of GPU-MetaD for uncovering complex rare events in realistic large-scale materials systems. |
| title | GPU-MetaD: Full-Life-Cycle GPU Accelerated Metadynamics with Machine Learning Potentials |
| topic | Computational Physics |
| url | https://arxiv.org/abs/2510.06873 |