Accurate molecular polarizabilities with coupled cluster theory and machine learning

Significance The dipole polarizability of molecules and materials is central to several physical phenomena, modeling techniques, and the interpretation of many experiments. Its accurate evaluation from first principles requires quantum chemistry methods that are often too demanding for routine use. The highly accurate calculations reported herein provide a much-needed benchmark of the accuracy of hybrid density functional theory (DFT) as well as training data for a machine-learning model that can predict the polarizability tensor with an error that is about 50% smaller than DFT. This framework provides an accurate, inexpensive, and transferable strategy for estimating the polarizabilities of molecules containing dozens of atoms, and therefore removes a considerable obstacle to accurate and reliable atomistic-based modeling of matter. 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.