Kernel charge equilibration: Efficient and accurate prediction of molecular dipole moments with a machine-learning enhanced electron density model
Publication Date
2022Journal Title
Machine Learning: Science and Technology
ISSN
2632-2153
Publisher
IOP Publishing
Volume
3
Issue
1
Language
en
Type
Article
This Version
VoR
Metadata
Show full item recordCitation
Staacke, C., Wengert, S., Kunkel, C., Csányi, G., Reuter, K., & Margraf, J. (2022). Kernel charge equilibration: Efficient and accurate prediction of molecular dipole moments with a machine-learning enhanced electron density model. Machine Learning: Science and Technology, 3 (1) https://doi.org/10.1088/2632-2153/ac568d
Abstract
<jats:title>Abstract</jats:title>
<jats:p>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.</jats:p>
Keywords
Paper, kernel, charge equilibration, partial charges, dipole moment
Sponsorship
Deutsche Forschungsgemeinschaft (EXC 2089/1-390776260, RE1509/18-2)
Identifiers
mlstac568d, ac568d, mlst-100464.r2
External DOI: https://doi.org/10.1088/2632-2153/ac568d
This record's URL: https://www.repository.cam.ac.uk/handle/1810/334880
Rights
Licence:
http://creativecommons.org/licenses/by/4.0
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