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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.
Bd. 3
(2022)
Heft 1
.
- 015032.
ISSN 2632-2153
DOI: https://doi.org/10.1088/2632-2153/ac568d
Abstract
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.
Weitere Angaben
| Publikationsform: | Artikel in einer Zeitschrift |
|---|---|
| Begutachteter Beitrag: | Ja |
| Institutionen der Universität: | Fakultäten > Fakultät für Biologie, Chemie und Geowissenschaften > Fachgruppe Chemie > Lehrstuhl Künstliche Intelligenz in der physiko-chemischen Materialanalytik Fakultäten > Fakultät für Biologie, Chemie und Geowissenschaften > Fachgruppe Chemie > Lehrstuhl Künstliche Intelligenz in der physiko-chemischen Materialanalytik > Lehrstuhl Künstliche Intelligenz in der physiko-chemischen Materialanalytik - Univ.-Prof. Dr. Johannes Theo Margraf |
| Titel an der UBT entstanden: | Nein |
| Themengebiete aus DDC: | 500 Naturwissenschaften und Mathematik > 540 Chemie |
| Eingestellt am: | 13 Nov 2023 13:54 |
| Letzte Änderung: | 13 Nov 2023 13:54 |
| URI: | https://eref.uni-bayreuth.de/id/eprint/87655 |