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). In this work, 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 (SA-GPR) framework, which assigns a (vector) dipole moment to each atom, while 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 (CCSD) 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. The ultimate performance of the models-and the optimal weighting of their combination-depend, 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; further, they define a challenging task to benchmark future models, particularly those aimed at the description of condensed phases.

Gaussian Process Regression for Materials and Molecules

“…Below each molecule, the black vector gives the molecular dipole moment calculated using the reference electronic-structure method (B3LYP-DFT), and the gray, green, or red vector gives the total GPR prediction. Adapted from ref ( 392 ).…”

Multi-scale approach for the prediction of atomic scale properties

“…Dielectric response functions are strongly affected by long-range correlations because of the cooperative nature of the underlying physical mechanism. Poor transferability of local models between structures of different sizes has been observed for molecular dipole moments, 41 polarizability, 40 and the electronic dielectric constant of bulk water. 39 For this purpose, we use a training set composed of 27 428 conformers of single aminoacids and 370 dipeptides, testing the predictions of the model on a smaller test set containing 30 dipeptides, 20 tripeptides, 16 tetrapeptides and 10 pentapeptide configurations.…”

Predicting second virial coefficients of organic and inorganic compounds using Gaussian process regression

“…al, which implements a partial-charge model to predict dipole moments of molecules. 31 The presence of a dipole moment in a molecule leads to a dipole-dipole interaction apart from the van der Waals interaction of non-polar molecules. The dipole moment of a molecule is proven to increase the attractive forces between molecules and therefore to lower B 2 for a given temperature.…”