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BIGDML—Towards accurate quantum machine learning force fields for materials

Machine-learning force fields (MLFF) should be accurate, computationally and data efficient, and applicable to molecules, materials, and interfaces thereof. Currently, MLFFs often introduce tradeoffs that restrict their practical applicability to small subsets of chemical space or require exhaustive...

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Autores principales: Sauceda, Huziel E., Gálvez-González, Luis E., Chmiela, Stefan, Paz-Borbón, Lauro Oliver, Müller, Klaus-Robert, Tkatchenko, Alexandre
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9243122/
https://www.ncbi.nlm.nih.gov/pubmed/35768400
http://dx.doi.org/10.1038/s41467-022-31093-x
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author Sauceda, Huziel E.
Gálvez-González, Luis E.
Chmiela, Stefan
Paz-Borbón, Lauro Oliver
Müller, Klaus-Robert
Tkatchenko, Alexandre
author_facet Sauceda, Huziel E.
Gálvez-González, Luis E.
Chmiela, Stefan
Paz-Borbón, Lauro Oliver
Müller, Klaus-Robert
Tkatchenko, Alexandre
author_sort Sauceda, Huziel E.
collection PubMed
description Machine-learning force fields (MLFF) should be accurate, computationally and data efficient, and applicable to molecules, materials, and interfaces thereof. Currently, MLFFs often introduce tradeoffs that restrict their practical applicability to small subsets of chemical space or require exhaustive datasets for training. Here, we introduce the Bravais-Inspired Gradient-Domain Machine Learning (BIGDML) approach and demonstrate its ability to construct reliable force fields using a training set with just 10–200 geometries for materials including pristine and defect-containing 2D and 3D semiconductors and metals, as well as chemisorbed and physisorbed atomic and molecular adsorbates on surfaces. The BIGDML model employs the full relevant symmetry group for a given material, does not assume artificial atom types or localization of atomic interactions and exhibits high data efficiency and state-of-the-art energy accuracies (errors substantially below 1 meV per atom) for an extended set of materials. Extensive path-integral molecular dynamics carried out with BIGDML models demonstrate the counterintuitive localization of benzene–graphene dynamics induced by nuclear quantum effects and their strong contributions to the hydrogen diffusion coefficient in a Pd crystal for a wide range of temperatures.
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spelling pubmed-92431222022-07-01 BIGDML—Towards accurate quantum machine learning force fields for materials Sauceda, Huziel E. Gálvez-González, Luis E. Chmiela, Stefan Paz-Borbón, Lauro Oliver Müller, Klaus-Robert Tkatchenko, Alexandre Nat Commun Article Machine-learning force fields (MLFF) should be accurate, computationally and data efficient, and applicable to molecules, materials, and interfaces thereof. Currently, MLFFs often introduce tradeoffs that restrict their practical applicability to small subsets of chemical space or require exhaustive datasets for training. Here, we introduce the Bravais-Inspired Gradient-Domain Machine Learning (BIGDML) approach and demonstrate its ability to construct reliable force fields using a training set with just 10–200 geometries for materials including pristine and defect-containing 2D and 3D semiconductors and metals, as well as chemisorbed and physisorbed atomic and molecular adsorbates on surfaces. The BIGDML model employs the full relevant symmetry group for a given material, does not assume artificial atom types or localization of atomic interactions and exhibits high data efficiency and state-of-the-art energy accuracies (errors substantially below 1 meV per atom) for an extended set of materials. Extensive path-integral molecular dynamics carried out with BIGDML models demonstrate the counterintuitive localization of benzene–graphene dynamics induced by nuclear quantum effects and their strong contributions to the hydrogen diffusion coefficient in a Pd crystal for a wide range of temperatures. Nature Publishing Group UK 2022-06-29 /pmc/articles/PMC9243122/ /pubmed/35768400 http://dx.doi.org/10.1038/s41467-022-31093-x Text en © The Author(s) 2022 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
Sauceda, Huziel E.
Gálvez-González, Luis E.
Chmiela, Stefan
Paz-Borbón, Lauro Oliver
Müller, Klaus-Robert
Tkatchenko, Alexandre
BIGDML—Towards accurate quantum machine learning force fields for materials
title BIGDML—Towards accurate quantum machine learning force fields for materials
title_full BIGDML—Towards accurate quantum machine learning force fields for materials
title_fullStr BIGDML—Towards accurate quantum machine learning force fields for materials
title_full_unstemmed BIGDML—Towards accurate quantum machine learning force fields for materials
title_short BIGDML—Towards accurate quantum machine learning force fields for materials
title_sort bigdml—towards accurate quantum machine learning force fields for materials
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9243122/
https://www.ncbi.nlm.nih.gov/pubmed/35768400
http://dx.doi.org/10.1038/s41467-022-31093-x
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