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

Journal of Chemical Theory and Computation
Chiara PanosettiJohannes T Margraf

Abstract

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 flex...Continue Reading

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Citations

Aug 19, 2020·Nature Communications·Alexandre Tkatchenko
Feb 20, 2021·Journal of Chemical Theory and Computation·Akshay Krishna Ammothum KandyJolla Kullgren
Apr 6, 2021·Journal of Chemical Theory and Computation·Lennard BöseltSereina Riniker
Jul 10, 2021·The Journal of Chemical Physics·Julia WestermayrReinhard J Maurer
Sep 23, 2020·The Journal of Physical Chemistry. a·Marc Philipp BahlkeCarmen Herrmann
Aug 14, 2020·The Journal of Physical Chemistry Letters·Martin StöhrAlexandre Tkatchenko
Jan 12, 2021·The Journal of Physical Chemistry. a·Chiara PanosettiChristoph Scheurer

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