Nuclear quantum delocalization enhances non-covalent intramolecular interactions: A machine learning and path integral molecular dynamics study
ORAL
Abstract
It is of common knowledge that nuclear quantum effects generates delocalized molecular dynamics. In this study, we present evidence that nuclear delocalization can enhance electronic and electrostatic interactions that promote localized dynamics. These results were obtained from the reconstructed potential-energy surfaces using the symmetrized gradient-domain machine learning (sGDML) framework[1] trained on coupled cluster with single, double, and perturbative triple excitations (CCSD(T)) data combined with path integral molecular dynamics simulations. The physical process responsible for this phenomenon is the effective reduction of the interatomic distances between non-covalently bonded atoms or functional groups. This potentiates intramolecular interactions such as the n→π* and electrostatic interactions[2]. These results diverge from the general assumption that nuclear quantum effects just tend to lower energetic barriers or to smoother the energy landscape, opening new avenues into possible explanations of complex processes in chemistry and biology.
[1] Chmiela et al. Sci. Adv. 3 (5), e1603015 (2017); Nat. Commun. 9 (1), 3887 (2108); Comput. Phys. Commun. 240, 38 (2019).
[2] Sauceda et al. J. Chem. Phys. 150 (11), 114102 (2019); arXiv:1909.08565 (2019).
[1] Chmiela et al. Sci. Adv. 3 (5), e1603015 (2017); Nat. Commun. 9 (1), 3887 (2108); Comput. Phys. Commun. 240, 38 (2019).
[2] Sauceda et al. J. Chem. Phys. 150 (11), 114102 (2019); arXiv:1909.08565 (2019).
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Presenters
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Huziel Sauceda
Tech Univ Berlin, Machine Learning Group, Technische Universität Berlin
Authors
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Huziel Sauceda
Tech Univ Berlin, Machine Learning Group, Technische Universität Berlin
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Valentin Vassilev Galindo
Physics and Materials Science Research Unit, University of Luxembourg, University of Luxembourg Limpertsberg
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Stefan Chmiela
Tech Univ Berlin, Machine Learning Group, Technische Universität Berlin
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Klaus-Robert Müller
Tech Univ Berlin, Machine Learning Group, Technische Universität Berlin
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Alexandre Tkatchenko
Physics and Materials Science Reasearch Unit, University of Luxembourg, Physics and Materials Science Research Unit, University of Luxembourg, University of Luxembourg, University of Luxembourg Limpertsberg