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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.23728 |
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| _version_ | 1866910899567067136 |
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| author | Fan, Cheng Li, Maodong Yuan, Sihao Xie, Zhaoxin Chen, Dechin Yang, Yi Isaac Gao, Yi Qin |
| author_facet | Fan, Cheng Li, Maodong Yuan, Sihao Xie, Zhaoxin Chen, Dechin Yang, Yi Isaac Gao, Yi Qin |
| contents | This study employed an artificial intelligence-enhanced molecular simulation framework to enable efficient Path Integral Molecular Dynamics (PIMD) simulations. Owing to its modular architecture and high-throughput capabilities, the framework effectively mitigates the computational complexity and resource-intensive limitations associated with conventional PIMD approaches. By integrating machine learning force fields (MLFFs) into the framework, we rigorously tested its performance through two representative cases: a small-molecule reaction system (double proton transfer in formic acid dimer) and a bulk-phase transition system (water-ice phase transformation). Computational results demonstrate that the proposed framework achieves accelerated PIMD simulations while preserving quantum mechanical accuracy. These findings show that nuclear quantum effects can be captured for complex molecular systems, using relatively low computational cost. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_23728 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Performing Path Integral Molecular Dynamics Using Artificial Intelligence Enhanced Molecular Simulation Framework Fan, Cheng Li, Maodong Yuan, Sihao Xie, Zhaoxin Chen, Dechin Yang, Yi Isaac Gao, Yi Qin Chemical Physics Computational Physics This study employed an artificial intelligence-enhanced molecular simulation framework to enable efficient Path Integral Molecular Dynamics (PIMD) simulations. Owing to its modular architecture and high-throughput capabilities, the framework effectively mitigates the computational complexity and resource-intensive limitations associated with conventional PIMD approaches. By integrating machine learning force fields (MLFFs) into the framework, we rigorously tested its performance through two representative cases: a small-molecule reaction system (double proton transfer in formic acid dimer) and a bulk-phase transition system (water-ice phase transformation). Computational results demonstrate that the proposed framework achieves accelerated PIMD simulations while preserving quantum mechanical accuracy. These findings show that nuclear quantum effects can be captured for complex molecular systems, using relatively low computational cost. |
| title | Performing Path Integral Molecular Dynamics Using Artificial Intelligence Enhanced Molecular Simulation Framework |
| topic | Chemical Physics Computational Physics |
| url | https://arxiv.org/abs/2503.23728 |