Accurate Molecular Polarizabilities with Coupled Cluster Theory and Machine Learning

ORAL

Abstract

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).

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Presenters

  • Yang Yang

    • Chemistry and Chemical Biology, Cornell University
    • Department of Chemistry and Chemical Biology, Cornell University
    • Department of Chemistry and Chemical Biology, Cornell University, Ithaca, NY

Authors

  • Yang Yang

    • Chemistry and Chemical Biology, Cornell University
    • Department of Chemistry and Chemical Biology, Cornell University
    • Department of Chemistry and Chemical Biology, Cornell University, Ithaca, NY

  • Ka Un Lao

    • Department of Chemistry and Chemical Biology, Cornell University

  • David M. Wilkins

    • Laboratory of Computational Science and Modeling, Institut des Matériaux, École Polytechnique Fédérale de Lausanne

  • Andrea Grisafi

    • École Polytechnique Federale de Lausanne
    • Laboratory of Computational Science and Modeling, Institut des Matériaux, École Polytechnique Fédérale de Lausanne

  • Michele Ceriotti

    • Ecole polytechnique federale de Lausanne
    • Ecole Polytechnique Federale de Lausanne
    • Institute of Materials, Swiss Federal Institute of Technology in Lausanne (EPFL), Switzerland
    • École Polytechnique Federale de Lausanne
    • Laboratory of Computational Science and Modeling, Institut des Matériaux, École Polytechnique Fédérale de Lausanne

  • Robert Distasio

    • Chemistry and Chemical Biology, Cornell University
    • Department of Chemistry and Chemical Biology, Cornell University
    • Cornell University
    • Department of Chemistry and Chemical Biology, Cornell University, Ithaca, NY