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 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.
A machine learning model for multipole coefficients of atom types H, C, O, N, S, F, and Cl in any molecular conformation is introduced, trained on quantum-chemical results for atoms in varying chemical environments drawn from thousands of organic molecules.
@article{Bereau_2015,
doi = {10.1021/acs.jctc.5b00301},
url = {https://doi.org/10.1021%2Facs.jctc.5b00301},
year = 2015,
month = {jun},
publisher = {American Chemical Society ({ACS})},
volume = {11},
number = {7},
pages = {3225--3233},
author = {Tristan Bereau and Denis Andrienko and O. Anatole von Lilienfeld},
title = {Transferable Atomic Multipole Machine Learning Models for Small Organic Molecules},
journal = {Journal of Chemical Theory and Computation}
}