Transferable Atomic Multipole Machine Learning Models for Small Organic Molecules

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...

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Bibliographic Details
Published in:Journal of chemical theory and computation 2015-07, Vol.11 (7), p.3225-3233
Main Authors: Bereau, Tristan, Andrienko, Denis, von Lilienfeld, O. Anatole
Format: Article
Language:English
Subjects:
INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
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Summary: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.
ISSN:1549-9618
1549-9626
DOI:10.1021/acs.jctc.5b00301