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 (LR-CCSD). 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.
|Number of pages||6|
|Journal||Proceedings of the National Academy of Sciences of the United States of America|
|Publication status||Published - 26 Feb 2019|
- Coupled Cluster Theory
- Density Functional Theory
- Dipole Polarizability
- Gaussian Process Regression
- Machine Learning
ASJC Scopus subject areas
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Dataset for "Quantum mechanical static dipole polarizabilities in the QM7b and AlphaML showcase databases"
Yang, Y. (Creator), Lao, K. U. (Creator), Wilkins, D. (Creator), Grisafi, A. (Creator), DiStasio, R. A. (Creator) & Ceriotti, M. (Creator), Materials Cloud, 2019