Accurate molecular polarizabilities with coupled cluster theory and machine learning

The molecular dipole polarizability describes the tendency of a molecule to change its dipole moment in response to an applied electric field. This quantity governs key intra- and intermolecular interactions, such as induction and dispersion; plays a vital role in determining the spectroscopic signa...

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Bibliographic Details
Published in:	Proceedings of the National Academy of Sciences - PNAS Vol. 116; no. 9; pp. 3401 - 3406
Main Authors:	Wilkins, David M., Grisafi, Andrea, Yang, Yang, Lao, Ka Un, DiStasio, Robert A., Ceriottia, Michele
Format:	Journal Article
Language:	English
Published:	United States National Academy of Sciences 26-02-2019
Subjects:	Amino acids Bases (nucleic acids) Carbohydrates Clusters Computer applications Coupling (molecular) Density functional theory Dipole moments Electric fields Electronic structure Isomers Learning algorithms Machine learning Organic chemistry Physical Sciences Polarizability Quantum mechanics Tensors Gaussian process regression coupled cluster theory machine learning dipole polarizability density functional theory
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Summary:	The molecular dipole polarizability describes the tendency of a molecule to change its dipole moment in response to an applied electric field. This quantity governs key intra- and intermolecular interactions, such as induction and dispersion; plays a vital role in determining the spectroscopic signatures of molecules; and is an essential ingredient in polarizable force fields. Compared with other ground-state properties, an accurate prediction of the molecular polarizability is considerably more difficult, as this response quantity is quite sensitive to the underlying electronic structure description. In this work, we present highly accurate quantum mechanical calculations of the static dipole polarizability tensors of 7,211 small organic molecules computed using linear response coupled cluster singles and doubles theory (LRCCSD). Using a symmetry-adapted machine-learning approach, we demonstrate that it is possible to predict the LR-CCSD molecular polarizabilities of these small molecules with an error that is an order of magnitude smaller than that of hybrid density functional theory (DFT) at a negligible computational cost. The resultant model is robust and transferable, yielding molecular polarizabilities for a diverse set of 52 larger molecules (including challenging conjugated systems, carbohydrates, small drugs, amino acids, nucleobases, and hydrocarbon isomers) at an accuracy that exceeds that of hybrid DFT. The atom-centered decomposition implicit in our machine-learning approach offers some insight into the shortcomings of DFT in the prediction of this fundamental quantity of interest.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Author contributions: D.M.W., A.G., Y.Y., K.U.L., R.A.D., and M.C. designed research; D.M.W., A.G., Y.Y., and K.U.L. performed research; D.M.W., A.G., Y.Y., K.U.L., R.A.D., and M.C. analyzed data; and D.M.W., A.G., Y.Y., K.U.L., R.A.D., and M.C. wrote the paper. Edited by Michael L. Klein, Institute of Computational Molecular Science, Temple University, Philadelphia, PA, and approved December 26, 2018 (received for review September 23, 2018)
ISSN:	0027-8424 1091-6490
DOI:	10.1073/pnas.1816132116