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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21 March 2019
Research Article|
March 18 2019
Molecular force fields with gradient-domain machine learning: Construction and application to dynamics of small molecules with coupled cluster forces
Huziel E. Sauceda
;
Huziel E. Sauceda
1
Fritz-Haber-Institut der Max-Planck-Gesellschaft
, 14195 Berlin, Germany
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Stefan Chmiela
;
Stefan Chmiela
2
Machine Learning Group, Technische Universität Berlin
, 10587 Berlin, Germany
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Igor Poltavsky
;
Igor Poltavsky
3
Physics and Materials Science Research Unit, University of Luxembourg
, L-1511 Luxembourg, Luxembourg
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Klaus-Robert Müller
;
Klaus-Robert Müller
a)
2
Machine Learning Group, Technische Universität Berlin
, 10587 Berlin, Germany
4
Department of Brain and Cognitive Engineering, Korea University
, Anam-dong, Seongbuk-gu, Seoul 02841, South Korea
5
Max Planck Institute for Informatics
, Stuhlsatzenhausweg, 66123 Saarbrücken, Germany
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Alexandre Tkatchenko
Alexandre Tkatchenko
b)
3
Physics and Materials Science Research Unit, University of Luxembourg
, L-1511 Luxembourg, Luxembourg
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J. Chem. Phys. 150, 114102 (2019)
Article history
Received:
October 27 2018
Accepted:
February 21 2019
Citation
Huziel E. Sauceda, Stefan Chmiela, Igor Poltavsky, Klaus-Robert Müller, Alexandre Tkatchenko; Molecular force fields with gradient-domain machine learning: Construction and application to dynamics of small molecules with coupled cluster forces. J. Chem. Phys. 21 March 2019; 150 (11): 114102. https://doi.org/10.1063/1.5078687
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