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Machine learning force fields based on local parametrization of dispersion interactions : application to the phase diagram of C60
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Muhli, Heikki, Chen, Xi, Bartók, Albert P., Hernández-León, Patricia, Csányi, Gábor, Ala-Nissila, Tapio and Caro, Miguel A. (2021) Machine learning force fields based on local parametrization of dispersion interactions : application to the phase diagram of C60. Physical Review B (Condensed Matter and Materials Physics), 104 (5). 054106 . doi:10.1103/PhysRevB.104.054106 ISSN 1098-0121.
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Official URL: https://doi.org/10.1103/PhysRevB.104.054106
Abstract
We present a comprehensive methodology to enable addition of van der Waals (vdW) corrections to machine learning (ML) atomistic force fields. Using a Gaussian approximation potential (GAP) [Bartók et al.., Phys. Rev. Lett. 104, 136403 (2010)] as baseline, we accurately machine learn a local model of atomic polarizabilities based on Hirshfeld volume partitioning of the charge density [Tkatchenko and Scheffler, Phys. Rev. Lett. 102, 073005 (2009)]. These environment-dependent polarizabilities are then used to parametrize a screened Londondispersion approximation to the vdW interactions. Our ML vdW model only needs to learn the charge density partitioning implicitly, by learning the reference Hirshfeld volumes from density functional theory (DFT). In practice, we can predict accurate Hirshfeld volumes from the knowledge of the local atomic environment (atomic positions) alone, making the model highly computationally efficient. For additional efficiency, our ML model of atomic polarizabilities reuses the same many-body atomic descriptors used for the underlying GAP learning of bonded interatomic interactions. We also show how the method enables straightforward computation of gradients of the observables, even when these remain challenging for the reference method (e.g., calculating gradients of the Hirshfeld volumes in DFT). Finally, we demonstrate the approach by studying the phase diagram of C60, where vdW effects are important. The need for a highly accurate vdW-inclusive reactive force field is highlighted by modeling the decomposition of the C60 molecules taking place at high pressures and temperatures.
Item Type: | Journal Article | |||||||||||||||||||||||||||
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Subjects: | Q Science > Q Science (General) Q Science > QC Physics |
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering Faculty of Science, Engineering and Medicine > Science > Physics |
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Library of Congress Subject Headings (LCSH): | Machine learning, Van der Waals forces, Heterostructures, Quantum theory, Gaussian processes | |||||||||||||||||||||||||||
Journal or Publication Title: | Physical Review B (Condensed Matter and Materials Physics) | |||||||||||||||||||||||||||
Publisher: | American Physical Society | |||||||||||||||||||||||||||
ISSN: | 1098-0121 | |||||||||||||||||||||||||||
Official Date: | 1 August 2021 | |||||||||||||||||||||||||||
Dates: |
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Volume: | 104 | |||||||||||||||||||||||||||
Number: | 5 | |||||||||||||||||||||||||||
Article Number: | 054106 | |||||||||||||||||||||||||||
DOI: | 10.1103/PhysRevB.104.054106 | |||||||||||||||||||||||||||
Status: | Peer Reviewed | |||||||||||||||||||||||||||
Publication Status: | Published | |||||||||||||||||||||||||||
Reuse Statement (publisher, data, author rights): | © 2021 American Physical Society | |||||||||||||||||||||||||||
Access rights to Published version: | Restricted or Subscription Access | |||||||||||||||||||||||||||
Copyright Holders: | ©2021 American Physical Society | |||||||||||||||||||||||||||
Date of first compliant deposit: | 20 July 2021 | |||||||||||||||||||||||||||
Date of first compliant Open Access: | 21 July 2021 | |||||||||||||||||||||||||||
RIOXX Funder/Project Grant: |
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