Kernel Charge Equilibration: Efficient and Accurate Prediction of Molecular Dipole Moments with a Machine-Learning Enhanced Electron Density Model
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Authors
Staacke, Carsten G
Wengert, Simon
Kunkel, Christian
Reuter, Karsten
Publication Date
2022-02-18Journal Title
Machine Learning: Science and Technology
ISSN
2632-2153
Publisher
IOP Publishing
Type
Article
This Version
AM
Metadata
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Staacke, C. G., Wengert, S., Kunkel, C., Csanyi, G., Reuter, K., & Margraf, J. T. (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 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 models like QEq, 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>
Identifiers
External DOI: https://doi.org/10.1088/2632-2153/ac568d
This record's URL: https://www.repository.cam.ac.uk/handle/1810/334785
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