Coarse-graining conformational dynamics with multi-dimensional generalized Langevin equation: how, when, and why
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866910600715567104 |
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| author | Xie, Pinchen Qiu, Yunrui E, Weinan |
| author_facet | Xie, Pinchen Qiu, Yunrui E, Weinan |
| contents | A data-driven ab initio generalized Langevin equation (AIGLE) approach is developed to learn and simulate high-dimensional, heterogeneous, coarse-grained conformational dynamics. Constrained by the fluctuation-dissipation theorem, the approach can build coarse-grained models in dynamical consistency with all-atom molecular dynamics. We also propose practical criteria for AIGLE to enforce long-term dynamical consistency. Case studies of a toy polymer, with 20 coarse-grained sites, and the alanine dipeptide, with two dihedral angles, elucidate why one should adopt AIGLE or its Markovian limit for modeling coarse-grained conformational dynamics in practice. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2405_12356 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Coarse-graining conformational dynamics with multi-dimensional generalized Langevin equation: how, when, and why Xie, Pinchen Qiu, Yunrui E, Weinan Biological Physics Machine Learning Chemical Physics Data Analysis, Statistics and Probability A data-driven ab initio generalized Langevin equation (AIGLE) approach is developed to learn and simulate high-dimensional, heterogeneous, coarse-grained conformational dynamics. Constrained by the fluctuation-dissipation theorem, the approach can build coarse-grained models in dynamical consistency with all-atom molecular dynamics. We also propose practical criteria for AIGLE to enforce long-term dynamical consistency. Case studies of a toy polymer, with 20 coarse-grained sites, and the alanine dipeptide, with two dihedral angles, elucidate why one should adopt AIGLE or its Markovian limit for modeling coarse-grained conformational dynamics in practice. |
| title | Coarse-graining conformational dynamics with multi-dimensional generalized Langevin equation: how, when, and why |
| topic | Biological Physics Machine Learning Chemical Physics Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2405.12356 |