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Efficient interatomic descriptors for accurate machine learning force fields of extended molecules
Machine learning force fields (MLFFs) are gradually evolving towards enabling molecular dynamics simulations of molecules and materials with ab initio accuracy but at a small fraction of the computational cost. However, several challenges remain to be addressed to enable predictive MLFF simulations...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10272221/ https://www.ncbi.nlm.nih.gov/pubmed/37322039 http://dx.doi.org/10.1038/s41467-023-39214-w |
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author | Kabylda, Adil Vassilev-Galindo, Valentin Chmiela, Stefan Poltavsky, Igor Tkatchenko, Alexandre |
author_facet | Kabylda, Adil Vassilev-Galindo, Valentin Chmiela, Stefan Poltavsky, Igor Tkatchenko, Alexandre |
author_sort | Kabylda, Adil |
collection | PubMed |
description | Machine learning force fields (MLFFs) are gradually evolving towards enabling molecular dynamics simulations of molecules and materials with ab initio accuracy but at a small fraction of the computational cost. However, several challenges remain to be addressed to enable predictive MLFF simulations of realistic molecules, including: (1) developing efficient descriptors for non-local interatomic interactions, which are essential to capture long-range molecular fluctuations, and (2) reducing the dimensionality of the descriptors to enhance the applicability and interpretability of MLFFs. Here we propose an automatized approach to substantially reduce the number of interatomic descriptor features while preserving the accuracy and increasing the efficiency of MLFFs. To simultaneously address the two stated challenges, we illustrate our approach on the example of the global GDML MLFF. We found that non-local features (atoms separated by as far as 15 Å in studied systems) are crucial to retain the overall accuracy of the MLFF for peptides, DNA base pairs, fatty acids, and supramolecular complexes. Interestingly, the number of required non-local features in the reduced descriptors becomes comparable to the number of local interatomic features (those below 5 Å). These results pave the way to constructing global molecular MLFFs whose cost increases linearly, instead of quadratically, with system size. |
format | Online Article Text |
id | pubmed-10272221 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-102722212023-06-17 Efficient interatomic descriptors for accurate machine learning force fields of extended molecules Kabylda, Adil Vassilev-Galindo, Valentin Chmiela, Stefan Poltavsky, Igor Tkatchenko, Alexandre Nat Commun Article Machine learning force fields (MLFFs) are gradually evolving towards enabling molecular dynamics simulations of molecules and materials with ab initio accuracy but at a small fraction of the computational cost. However, several challenges remain to be addressed to enable predictive MLFF simulations of realistic molecules, including: (1) developing efficient descriptors for non-local interatomic interactions, which are essential to capture long-range molecular fluctuations, and (2) reducing the dimensionality of the descriptors to enhance the applicability and interpretability of MLFFs. Here we propose an automatized approach to substantially reduce the number of interatomic descriptor features while preserving the accuracy and increasing the efficiency of MLFFs. To simultaneously address the two stated challenges, we illustrate our approach on the example of the global GDML MLFF. We found that non-local features (atoms separated by as far as 15 Å in studied systems) are crucial to retain the overall accuracy of the MLFF for peptides, DNA base pairs, fatty acids, and supramolecular complexes. Interestingly, the number of required non-local features in the reduced descriptors becomes comparable to the number of local interatomic features (those below 5 Å). These results pave the way to constructing global molecular MLFFs whose cost increases linearly, instead of quadratically, with system size. Nature Publishing Group UK 2023-06-15 /pmc/articles/PMC10272221/ /pubmed/37322039 http://dx.doi.org/10.1038/s41467-023-39214-w Text en © The Author(s) 2023, corrected publication 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Kabylda, Adil Vassilev-Galindo, Valentin Chmiela, Stefan Poltavsky, Igor Tkatchenko, Alexandre Efficient interatomic descriptors for accurate machine learning force fields of extended molecules |
title | Efficient interatomic descriptors for accurate machine learning force fields of extended molecules |
title_full | Efficient interatomic descriptors for accurate machine learning force fields of extended molecules |
title_fullStr | Efficient interatomic descriptors for accurate machine learning force fields of extended molecules |
title_full_unstemmed | Efficient interatomic descriptors for accurate machine learning force fields of extended molecules |
title_short | Efficient interatomic descriptors for accurate machine learning force fields of extended molecules |
title_sort | efficient interatomic descriptors for accurate machine learning force fields of extended molecules |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10272221/ https://www.ncbi.nlm.nih.gov/pubmed/37322039 http://dx.doi.org/10.1038/s41467-023-39214-w |
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