SKM 2023 – wissenschaftliches Programm

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MM: Fachverband Metall- und Materialphysik

MM 8: Development of Computational Methods: Diverse Topics and Machine Learning

MM 8.7: Vortrag

Montag, 27. März 2023, 17:30–17:45, SCH A 251

Kernel Charge Equilibration: Machine Learned Interatomic Potentials With Full Long-Range Electrostatics — •Martin Vondrak, Johannes T. Margraf, and Karsten Reuter — Fritz Haber Institute of the Max Planck Society, Berlin, Germany

Machine learning (ML) techniques have recently been shown to bridge the gap between accurate first-principles methods and computationally cheap empirical potentials. This is achieved by learning a mapping between a systems structure its physical properties. State-of-the-art models typically represent chemical structures in terms of local atomic environments to this end. This inevitably leads to the neglect of long-range interactions (most prominently electrostatics) and non-local phenomena (e.g. charge transfer), resulting in significant errors in the description of polar molecules and materials (particularly in non-isotropic environments). To overcome these issues, we recently proposed a ML framework for predicting charge distributions in molecules termed Kernel Charge Equilibration (kQEq). Here, atomic charges are derived from a physical model using environment-dependent atomic electronegativities. In this contributions, strategies for creating kQEq interatomic potentials are discussed, including the combination of short-ranged Gaussian Approximation Potentials with kQEq.