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Transferable Atomic Multipole Machine Learning Models for Small Organic Molecules

Bereau, Tristan and Andrienko, Denis and von Lilienfeld, O. Anatole. (2015) Transferable Atomic Multipole Machine Learning Models for Small Organic Molecules. Journal of Chemical Theory and Computation, 11 (7). pp. 3225-3233.

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Official URL: http://edoc.unibas.ch/43347/

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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.
Faculties and Departments:05 Faculty of Science > Departement Chemie > Chemie > Physikalische Chemie (Lilienfeld)
UniBasel Contributors:von Lilienfeld, Anatole
Item Type:Article, refereed
Article Subtype:Research Article
Publisher:American Chemical Society
ISSN:1549-9618
e-ISSN:1549-9626
Note:Publication type according to Uni Basel Research Database: Journal article
Identification Number:
Last Modified:12 Apr 2017 11:34
Deposited On:20 Jun 2016 08:57

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