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Learning to Use the Force: Fitting Repulsive Potentials in Density-Functional Tight-Binding with Gaussian Process Regression

Title data

Panosetti, Chiara ; Engelmann, Artur ; Nemec, Lydia ; Reuter, Karsten ; Margraf, Johannes T.:
Learning to Use the Force: Fitting Repulsive Potentials in Density-Functional Tight-Binding with Gaussian Process Regression.
In: Journal of Chemical Theory and Computation. Vol. 16 (2020) Issue 4 . - pp. 2181-2191.
ISSN 1549-9626
DOI: https://doi.org/10.1021/acs.jctc.9b00975

Abstract in another language

The Density-Functional Tight Binding (DFTB) method is a popular semiempirical approximation to Density Functional Theory (DFT). In many cases, DFTB can provide comparable accuracy to DFT at a fraction of the cost, enabling simulations on length and time scales that are unfeasible with first-principles DFT. At the same time (and in contrast to empirical interatomic potentials and force fields), DFTB still offers direct access to electronic properties such as the band structure. These advantages come at the cost of introducing empirical parameters to the method, leading to a reduced transferability compared to true first-principle approaches. Consequently, it would be very useful if the parameter sets could be routinely adjusted for a given project. While fairly robust and transferable parametrization workflows exist for the electronic structure part of DFTB, the so-called repulsive potential Vrep poses a major challenge. In this paper, we propose a machine-learning (ML) approach to fitting Vrep, using Gaussian Process Regression (GPR) to reconstruct Vrep with DFT-DFTB force residues as training data. The use of GPR circumvents the need for nonlinear or global parameter optimization, while at the same time offering arbitrary flexibility in terms of the functional form. We also show that the proposed method can be applied to multiple elements at once, by fitting repulsive potentials for organic molecules containing carbon, hydrogen, and oxygen. Overall, the new approach removes focus from the choice of functional form and parametrization procedure, in favor of a data-driven philosophy.

Further data

Item Type: Article in a journal
Refereed: Yes
Institutions of the University: Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Chemistry > Chair Künstliche Intelligenz in der physiko-chemischen Materialanalytik
Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Chemistry > Chair Künstliche Intelligenz in der physiko-chemischen Materialanalytik > Chair Künstliche Intelligenz in der physiko-chemischen Materialanalytik - Univ.-Prof. Dr. Johannes Theo Margraf
Result of work at the UBT: No
DDC Subjects: 500 Science > 540 Chemistry
Date Deposited: 13 Nov 2023 12:49
Last Modified: 13 Nov 2023 12:49
URI: https://eref.uni-bayreuth.de/id/eprint/87674