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Transferable Atomic Multipole Machine Learning Models for Small Organic Molecules

Type of publication Peer-reviewed
Publikationsform Original article (peer-reviewed)
Author Tristan Bereau Denis Andrienko O. Anatole von Lilienfeld,
Project From atomistic exploration of chemical compound space towards bio-molecular design: Quantum mechanical rational compound design (QM-RCD)
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Original article (peer-reviewed)

Journal Journal of Chemical Theory and Computation
Page(s) 3225
Title of proceedings Journal of Chemical Theory and Computation
DOI 10.1021/acs.jctc.5b00301

Abstract

Accurate representation of the molecular electrostatic potential, which is often expanded in distributed multipole moments, is crucial for an efficient evaluation of intermolecular interactions. Here we introduce a machine learning model for multipole coefficients of atom types H, C, O, N, S, F, and Cl in any molecular conformation. The model is trained on quantum-chemical results for atoms in varying chemical environments drawn from thousands of organic molecules. Multipoles in systems with neutral, cationic, and anionic molecular charge states are treated with individual models. The models’ predictive accuracy and applicability are illustrated by evaluating intermolecular interaction energies of nearly 1,000 dimers and the cohesive energy of the benzene crystal.
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