High-fidelity molecular dynamics trajectory reconstruction with bi-directional neural networks
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Winkler, Ludwig | - |
dc.contributor.author | Mueller, Klaus-Robert | - |
dc.contributor.author | Sauceda, Huziel E. | - |
dc.date.accessioned | 2022-08-13T02:40:48Z | - |
dc.date.available | 2022-08-13T02:40:48Z | - |
dc.date.created | 2022-08-12 | - |
dc.date.issued | 2022-06-01 | - |
dc.identifier.issn | 2632-2153 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/142977 | - |
dc.description.abstract | Molecular dynamics (MD) simulations are a cornerstone in science, enabling the investigation of a system's thermodynamics all the way to analyzing intricate molecular interactions. In general, creating extended molecular trajectories can be a computationally expensive process, for example, when running ab-initio simulations. Hence, repeating such calculations to either obtain more accurate thermodynamics or to get a higher resolution in the dynamics generated by a fine-grained quantum interaction can be time- and computational resource-consuming. In this work, we explore different machine learning methodologies to increase the resolution of MD trajectories on-demand within a post-processing step. As a proof of concept, we analyse the performance of bi-directional neural networks (NNs) such as neural ODEs, Hamiltonian networks, recurrent NNs and long short-term memories, as well as the uni-directional variants as a reference, for MD simulations (here: the MD17 dataset). We have found that Bi-LSTMs are the best performing models; by utilizing the local time-symmetry of thermostated trajectories they can even learn long-range correlations and display high robustness to noisy dynamics across molecular complexity. Our models can reach accuracies of up to 10(-4) angstrom in trajectory interpolation, which leads to the faithful reconstruction of several unseen high-frequency molecular vibration cycles. This renders the comparison between the learned and reference trajectories indistinguishable. The results reported in this work can serve (1) as a baseline for larger systems, as well as (2) for the construction of better MD integrators. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | IOP Publishing Ltd | - |
dc.title | High-fidelity molecular dynamics trajectory reconstruction with bi-directional neural networks | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Mueller, Klaus-Robert | - |
dc.identifier.doi | 10.1088/2632-2153/ac6ec6 | - |
dc.identifier.scopusid | 2-s2.0-85131679246 | - |
dc.identifier.wosid | 000802740000001 | - |
dc.identifier.bibliographicCitation | MACHINE LEARNING-SCIENCE AND TECHNOLOGY, v.3, no.2 | - |
dc.relation.isPartOf | MACHINE LEARNING-SCIENCE AND TECHNOLOGY | - |
dc.citation.title | MACHINE LEARNING-SCIENCE AND TECHNOLOGY | - |
dc.citation.volume | 3 | - |
dc.citation.number | 2 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Science & Technology - Other Topics | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Interdisciplinary Applications | - |
dc.relation.journalWebOfScienceCategory | Multidisciplinary Sciences | - |
dc.subject.keywordAuthor | super-resolution | - |
dc.subject.keywordAuthor | molecular dynamics | - |
dc.subject.keywordAuthor | bi-directional recurrent neural networks | - |
dc.subject.keywordAuthor | trajectory learning | - |
dc.subject.keywordAuthor | LSTM | - |
dc.subject.keywordAuthor | machine learning | - |
dc.subject.keywordAuthor | molecular systems | - |
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