Accurate Molecular Polarizabilities with Coupled Cluster Theory and Machine Learning

Despite the importance of the molecular dipole polarizability in governing key intra- and inter-molecular interactions (such as induction and dispersion), determining the spectroscopic signatures of molecules, and being an essential ingredient in polarizable force fields, an accurate and computationally efficient prediction of this fundamental quantum mechanical response property still remains a challenge to date. In this work, we present a benchmark database [1] of highly accurate 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 [2], we also demonstrate that it is possible to predict these LR-CCSD polarizabilities with an error that is an order of magnitude smaller than that of hybrid density functional theory (DFT). The resulting AlphaML model is robust and transferable, and able to yield molecular polarizabilities for a diverse set of 52 larger molecules (including challenging conjugated systems, carbohydrates, small drugs, amino acids, nucleobases, and hydrocarbon isomers) with a similar level of accuracy and at a negligible computational cost.
[1] Sci Data 6, 152 (2019).
[2] Proc Natl Acad Sci USA 116, 3401 (2019).