Learning the Quantum Centroid Force Correction in Molecular Systems: A Localized Approach
Molecular mechanics (MM) is a powerful tool to study the properties of molecular systems in the fields of biology and materials science. With the development of ab initio force field and the application of ab initio potential energy surface, the nuclear quantum effect (NQE) is becoming increasingly...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-05-01
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| Series: | Frontiers in Molecular Biosciences |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmolb.2022.851311/full |
| _version_ | 1811233447134363648 |
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| author | Chuixiong Wu Ruye Li Kuang Yu |
| author_facet | Chuixiong Wu Ruye Li Kuang Yu |
| author_sort | Chuixiong Wu |
| collection | DOAJ |
| description | Molecular mechanics (MM) is a powerful tool to study the properties of molecular systems in the fields of biology and materials science. With the development of ab initio force field and the application of ab initio potential energy surface, the nuclear quantum effect (NQE) is becoming increasingly important for the robustness of the simulation. However, the state-of-the-art path-integral molecular dynamics simulation, which incorporates NQE in MM, is still too expensive to conduct for most biological and material systems. In this work, we analyze the locality of NQE, using both analytical and numerical approaches, and conclude that NQE is an extremely localized phenomenon in nonreactive molecular systems. Therefore, we can use localized machine learning (ML) models to predict quantum force corrections both accurately and efficiently. Using liquid water as example, we show that the ML facilitated centroid MD can reproduce the NQEs in both the thermodynamical and the dynamical properties, with a minimal increase in computational time compared to classical molecular dynamics. This simple approach thus largely decreases the computational cost of quantum simulations, making it really accessible to the studies of large-scale molecular systems. |
| first_indexed | 2024-04-12T11:20:23Z |
| format | Article |
| id | doaj.art-bbcc1bbc21094b27b354b925c8064452 |
| institution | Directory Open Access Journal |
| issn | 2296-889X |
| language | English |
| last_indexed | 2024-04-12T11:20:23Z |
| publishDate | 2022-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Molecular Biosciences |
| spelling | doaj.art-bbcc1bbc21094b27b354b925c80644522022-12-22T03:35:24ZengFrontiers Media S.A.Frontiers in Molecular Biosciences2296-889X2022-05-01910.3389/fmolb.2022.851311851311Learning the Quantum Centroid Force Correction in Molecular Systems: A Localized ApproachChuixiong WuRuye LiKuang YuMolecular mechanics (MM) is a powerful tool to study the properties of molecular systems in the fields of biology and materials science. With the development of ab initio force field and the application of ab initio potential energy surface, the nuclear quantum effect (NQE) is becoming increasingly important for the robustness of the simulation. However, the state-of-the-art path-integral molecular dynamics simulation, which incorporates NQE in MM, is still too expensive to conduct for most biological and material systems. In this work, we analyze the locality of NQE, using both analytical and numerical approaches, and conclude that NQE is an extremely localized phenomenon in nonreactive molecular systems. Therefore, we can use localized machine learning (ML) models to predict quantum force corrections both accurately and efficiently. Using liquid water as example, we show that the ML facilitated centroid MD can reproduce the NQEs in both the thermodynamical and the dynamical properties, with a minimal increase in computational time compared to classical molecular dynamics. This simple approach thus largely decreases the computational cost of quantum simulations, making it really accessible to the studies of large-scale molecular systems.https://www.frontiersin.org/articles/10.3389/fmolb.2022.851311/fullmolecular dynamicsmachine learningnuclear quantum effectspath-integral molecular dynamicscentroid molecular dynamics |
| spellingShingle | Chuixiong Wu Ruye Li Kuang Yu Learning the Quantum Centroid Force Correction in Molecular Systems: A Localized Approach Frontiers in Molecular Biosciences molecular dynamics machine learning nuclear quantum effects path-integral molecular dynamics centroid molecular dynamics |
| title | Learning the Quantum Centroid Force Correction in Molecular Systems: A Localized Approach |
| title_full | Learning the Quantum Centroid Force Correction in Molecular Systems: A Localized Approach |
| title_fullStr | Learning the Quantum Centroid Force Correction in Molecular Systems: A Localized Approach |
| title_full_unstemmed | Learning the Quantum Centroid Force Correction in Molecular Systems: A Localized Approach |
| title_short | Learning the Quantum Centroid Force Correction in Molecular Systems: A Localized Approach |
| title_sort | learning the quantum centroid force correction in molecular systems a localized approach |
| topic | molecular dynamics machine learning nuclear quantum effects path-integral molecular dynamics centroid molecular dynamics |
| url | https://www.frontiersin.org/articles/10.3389/fmolb.2022.851311/full |
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