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Regensburg 2019 – scientific programme

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MM: Fachverband Metall- und Materialphysik

MM 10: Methods in Computational Materials Modelling (methodological aspects, numerics)

MM 10.5: Talk

Monday, April 1, 2019, 16:45–17:00, H45

Learning to use the force: DFTB repulsion with Gaussian Process RegressionArtur Engelmann, •Chiara Panosetti, Johannes T. Margraf, and Karsten Reuter — Chair for Theoretical Chemistry, Technische Universität München, Germany

Density-Functional Tight Binding (DFTB) is increasingly popular among computational modellists as it provides comparable accuracy to DFT at a fraction of the cost, enabling large scale simulations while retaining direct access to electronic structure properties. Yet, a bottleneck to this day remains the difficulty to parametrize the interactions for large subsets of atoms across the periodic table, let alone an extensive, universal parametrization. Especially challenging is the parametrization of the pairwise repulsion: an unescapable N2 effort, and a cumbersome one. Most schemes involve fitting the repulsion to some analytical potential by e.g. minimizing force residues. However, this presents a number of limitations, such as constraints on the reference geometries, and the necessity of projecting forces along the bonds. Further, any predefined functional form not only carries a certain degree of arbitrary bias in its very choice, but may also lack the flexibility to capture subtle features around equilibrium distances (where the “repulsive” potential may as well be attractive). We thereby propose to rather machine-learn the repulsive force, using Gaussian Process Regression similarly to the generation of GAP potentials [1]. We discuss a proof-of-principle application on carbon, showing how such an approach removes all the above limitations at once.

[1] A.P. Bartók et al., PRL 104, 136403 (2010)

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