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  Molecular force fields with gradient-domain machine learning: Construction and application to dynamics of small molecules with coupled cluster forces

Sauceda, H. E., Chmiela, S., Poltavsky, I., Müller, K.-R., & Tkatchenko, A. (2019). Molecular force fields with gradient-domain machine learning: Construction and application to dynamics of small molecules with coupled cluster forces. The Journal of Chemical Physics, 150(11): 114102. doi:10.1063/1.5078687.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0003-5307-8 Version Permalink: http://hdl.handle.net/21.11116/0000-0005-8DAE-9
Genre: Journal Article

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1901.06594.pdf (Preprint), 5MB
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 Creators:
Sauceda, Huziel E.1, Author              
Chmiela, Stefan2, Author
Poltavsky, Igor3, Author
Müller, Klaus-Robert2, 4, 5, Author
Tkatchenko, Alexandre3, Author
Affiliations:
1Theory, Fritz Haber Institute, Max Planck Society, ou_634547              
2Machine Learning Group, Technische Universität Berlin, 10587 Berlin, Germany, ou_persistent22              
3Physics and Materials Science Research Unit, University of Luxembourg, ou_persistent22              
4Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society, ou_40046              
5Department of Brain and Cognitive Engineering, Korea University, Anam-dong, Seongbuk-gu, Seoul 02841, South Korea, ou_persistent22              

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 Abstract: We present the construction of molecular force fields for small molecules (less than 25 atoms) using the recently developed symmetrized gradient-domain machine learning (sGDML) approach [Chmiela et al., Nat. Commun. 9, 3887 (2018) and Chmiela et al., Sci. Adv. 3, e1603015 (2017)]. This approach is able to accurately reconstruct complex high-dimensional potential-energy surfaces from just a few 100s of molecular conformations extracted from ab initio molecular dynamics trajectories. The data efficiency of the sGDML approach implies that atomic forces for these conformations can be computed with high-level wavefunction-based approaches, such as the “gold standard” coupled-cluster theory with single, double and perturbative triple excitations [CCSD(T)]. We demonstrate that the flexible nature of the sGDML model recovers local and non-local electronic interactions (e.g., H-bonding, proton transfer, lone pairs, changes in hybridization states, steric repulsion, and n → π* interactions) without imposing any restriction on the nature of interatomic potentials. The analysis of sGDML molecular dynamics trajectories yields new qualitative insights into dynamics and spectroscopy of small molecules close to spectroscopic accuracy.

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Language(s): eng - English
 Dates: 2018-10-272019-01-212019-03-182019-03-21
 Publication Status: Published in print
 Pages: 13
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1063/1.5078687
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Project name : BeStMo - Beyond Static Molecules: Modeling Quantum Fluctuations in Complex Molecular Environments
Grant ID : 725291
Funding program : Horizon 2020 (H2020)
Funding organization : European Commission (EC)

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Title: The Journal of Chemical Physics
  Other : J. Chem. Phys.
Source Genre: Journal
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Publ. Info: Woodbury, N.Y. : American Institute of Physics
Pages: 13 Volume / Issue: 150 (11) Sequence Number: 114102 Start / End Page: - Identifier: ISSN: 0021-9606
CoNE: https://pure.mpg.de/cone/journals/resource/954922836226