title: Transferable Atomic Multipole Machine Learning Models for Small Organic Molecules.
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.
authors.affiliation: Max Planck Institute for Polymer Research , Ackermannweg 10, 55128 Mainz, Germany.
authors.forename: Tristan
authors.initials: T
authors.lastname: Bereau
authors.affiliation: Max Planck Institute for Polymer Research , Ackermannweg 10, 55128 Mainz, Germany.
authors.forename: Denis
authors.initials: D
authors.lastname: Andrienko
authors.affiliation: Institute of Physical Chemistry and National Center for Computational Design and Discovery of Novel Materials, Department of Chemistry, University of Basel , 4056 Basel, Switzerland.|Argonne Leadership Computing Facility, Argonne National Laboratory , Argonne, Illinois 60439, United States.
authors.forename: O Anatole
authors.initials: OA
authors.lastname: von Lilienfeld