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

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Peer-reviewed

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Article

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Authors

Staacke, Carsten G 
Wengert, Simon 
Kunkel, Christian 
Reuter, Karsten 

Abstract

jats:titleAbstract</jats:title> jats:pState-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>

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Journal Title

Machine Learning: Science and Technology

Conference Name

Journal ISSN

2632-2153
2632-2153

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Publisher

IOP Publishing