Title: Predicting molecular dipole moments by combining atomic partial charges and atomic dipoles

Abstract

The molecular dipole moment (μ) is a central quantity in chemistry. It is essential in predicting infrared and sum-frequency generation spectra as well as induction and long-range electrostatic interactions. Furthermore, it can be extracted directly—via the ground state electron density—from high-level quantum mechanical calculations, making it an ideal target for machine learning (ML). Here, we choose to represent this quantity with a physically inspired ML model that captures two distinct physical effects: local atomic polarization is captured within the symmetry-adapted Gaussian process regression framework which assigns a (vector) dipole moment to each atom, while the movement of charge across the entire molecule is captured by assigning a partial (scalar) charge to each atom. The resulting “MuML” models are fitted together to reproduce molecular μ computed using high-level coupled-cluster theory and density functional theory (DFT) on the QM7b dataset, achieving more accurate results due to the physics-based combination of these complementary terms. The combined model shows excellent transferability when applied to a showcase dataset of larger and more complex molecules, approaching the accuracy of DFT at a small fraction of the computational cost. We also demonstrate that the uncertainty in the predictions can be estimated reliably using a calibrated committee model.more » The ultimate performance of the models—and the optimal weighting of their combination—depends, however, on the details of the system at hand, with the scalar model being clearly superior when describing large molecules whose dipole is almost entirely generated by charge separation. These observations point to the importance of simultaneously accounting for the local and non-local effects that contribute to μ; furthermore, they define a challenging task to benchmark future models, particularly those aimed at the description of condensed phases.« less

Authors:

Veit, Max  ^[1];

• Search DOE PAGES for author "Veit, Max"
• Search DOE PAGES for ORCID "0000-0001-7813-4015"

Wilkins, David M.  ^[1];

• Search DOE PAGES for author "Wilkins, David M."
• Search DOE PAGES for ORCID "0000-0003-3739-5512"

Yang, Yang  ^[2];

• Search DOE PAGES for author "Yang, Yang"
• Search DOE PAGES for ORCID "0000-0002-6859-4998"

DiStasio, Robert A.  ^[2];

• Search DOE PAGES for author "DiStasio, Robert A."
• Search DOE PAGES for ORCID "0000-0003-2732-194X"

Ceriotti, Michele  ^[1]

• Search DOE PAGES for author "Ceriotti, Michele"
• Search DOE PAGES for ORCID "0000-0003-2571-2832"

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+ Show Author Affiliations

1. Ecole Polytechnique Federale Lausanne (Switzlerland). Lab. of Computational Science and Modeling, IMX
2. Cornell Univ., Ithaca, NY (United States). Dept. of Chemistry and Chemical Biology

Publication Date:

Thu Jul 09 00:00:00 EDT 2020

Research Org.:

Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)

Sponsoring Org.:

USDOE Office of Science (SC); European Research Council (ERC)

OSTI Identifier:

1681219

Alternate Identifier(s):

OSTI ID: 1637990

Grant/Contract Number:  

AC02-05CH11231; 677013-HBMAP

Resource Type:

Accepted Manuscript

Journal Name:

Journal of Chemical Physics

Additional Journal Information:

Journal Volume: 153; Journal Issue: 2; Journal ID: ISSN 0021-9606

Publisher:

American Institute of Physics (AIP)

Country of Publication:

United States

Language:

English

Subject:

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; Gaussian processes; machine learning; polarization; electrostatics; polypeptide; density functional theory; coupled-cluster methods; molecular dipole moments; infrared spectroscopy; sum-frequency generation