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Kernel Charge Equilibration: Efficient and Accurate Prediction of Molecular Dipole Moments with a Machine-Learning Enhanced Electron Density Model

Title data

Staacke, Carsten G. ; Wengert, Simon ; Kunkel, Christian ; Csányi, Gábor ; Reuter, Karsten ; Margraf, Johannes T.:
Kernel Charge Equilibration: Efficient and Accurate Prediction of Molecular Dipole Moments with a Machine-Learning Enhanced Electron Density Model.
In: Machine Learning: Science and Technology. Vol. 3 (2022) Issue 1 . - 015032.
ISSN 2632-2153
DOI: https://doi.org/10.1088/2632-2153/ac568d

Abstract in another language

State-of-the-art machine learning (ML) interatomic potentials use local representations of atomic environments to ensure linear scaling and size-extensivity. This implies a neglect of long-range interactions, most prominently related to electrostatics. To overcome this limitation, we herein present a ML framework for predicting charge distributions and their interactions termed kernel charge equilibration (kQEq). This model is based on classical charge equilibration (QEq) models expanded with an environment-dependent electronegativity. In contrast to previously reported neural network models with a similar concept, kQEq takes advantage of the linearity of both QEq and Kernel Ridge Regression to obtain a closed-form linear algebra expression for training the models. Furthermore, we avoid the ambiguity of charge partitioning schemes by using dipole moments as reference data. As a first application, we show that kQEq can be used to generate accurate and highly data-efficient models for molecular dipole moments.

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 13:54
Last Modified: 13 Nov 2023 13:54
URI: https://eref.uni-bayreuth.de/id/eprint/87655