Trillion-atom molecular dynamics simulations with ab initio accuracy
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | |
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| _version_ | 1866913066745069568 |
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| author | Suo, Pengfei Cao, Wudi Wu, Xingxing Zhang, Wenjie Fan, Zheyong Xian, Shuanghan Wang, Rui Qian, Cheng Liang, Chao Yuan, Qinghong Chen, Xiaoshuang Guan, Pengfei Bu, Jingde Tian, Hongzhen Su, Yanjing Ding, Feng Wang, Lin-Wang |
| author_facet | Suo, Pengfei Cao, Wudi Wu, Xingxing Zhang, Wenjie Fan, Zheyong Xian, Shuanghan Wang, Rui Qian, Cheng Liang, Chao Yuan, Qinghong Chen, Xiaoshuang Guan, Pengfei Bu, Jingde Tian, Hongzhen Su, Yanjing Ding, Feng Wang, Lin-Wang |
| contents | Material properties are fundamentally dictated by multiscale phenomena, which often reach mesoscale in size. The μm mesoscale is also the size which can be observed directly under an optical microscope, bridging the atomistic microscopic description with the continuous model macroscopic world. In this work, we report an unprecedented molecular dynamics (MD) simulation comprising 1.62 trillion atoms. Utilizing the neuroevolution potential (NEP) framework, we attained ab initio accuracy on China's New-generation Intelligent Supercomputer. Our implementation achieves a time-to-solution (s/step/atom) 100 times faster than previous state-of-the-art machine learning force field simulations, and 1,000 times faster than the Gordon Bell Prize-winning application from six years ago. Furthermore, we demonstrate an 86.9% weak scaling efficiency from a single GPGPU to 45,000 GPGPUs. These results redefine atomistic simulation boundaries, enabling direct mesoscopic modeling with quantum-level precision. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_24816 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Trillion-atom molecular dynamics simulations with ab initio accuracy Suo, Pengfei Cao, Wudi Wu, Xingxing Zhang, Wenjie Fan, Zheyong Xian, Shuanghan Wang, Rui Qian, Cheng Liang, Chao Yuan, Qinghong Chen, Xiaoshuang Guan, Pengfei Bu, Jingde Tian, Hongzhen Su, Yanjing Ding, Feng Wang, Lin-Wang Materials Science Material properties are fundamentally dictated by multiscale phenomena, which often reach mesoscale in size. The μm mesoscale is also the size which can be observed directly under an optical microscope, bridging the atomistic microscopic description with the continuous model macroscopic world. In this work, we report an unprecedented molecular dynamics (MD) simulation comprising 1.62 trillion atoms. Utilizing the neuroevolution potential (NEP) framework, we attained ab initio accuracy on China's New-generation Intelligent Supercomputer. Our implementation achieves a time-to-solution (s/step/atom) 100 times faster than previous state-of-the-art machine learning force field simulations, and 1,000 times faster than the Gordon Bell Prize-winning application from six years ago. Furthermore, we demonstrate an 86.9% weak scaling efficiency from a single GPGPU to 45,000 GPGPUs. These results redefine atomistic simulation boundaries, enabling direct mesoscopic modeling with quantum-level precision. |
| title | Trillion-atom molecular dynamics simulations with ab initio accuracy |
| topic | Materials Science |
| url | https://arxiv.org/abs/2604.24816 |