A data-driven approach to determine dipole moments of diatomic molecules

TL;DR

This paper introduces a data-driven method using Gaussian process regression to accurately predict the dipole moments of diatomic molecules based on atomic properties, achieving less than 5% relative error.

Contribution

It presents the first comprehensive dataset and demonstrates that molecular dipole moments can be effectively predicted from atomic features using machine learning.

Findings

Dipole moments depend on atomic electron affinity and ionization potential.

The model achieves a relative error of less than 5%.

The dataset includes 162 diatomic molecules, the most extensive to date.

Abstract

We present a data-driven approach for the prediction of the electric dipole moment of diatomic molecules, which is one of the most relevant molecular properties. In particular, we apply Gaussian process regression to a novel dataset to show that dipole moments of diatomic molecules can be learned, and hence predicted, with a relative error <5%. The dataset contains the dipole moment of 162 diatomic molecules, the most exhaustive and unbiased dataset of dipole moments up to date. Our findings show that the dipole moment of diatomic molecules depends on atomic properties of the constituents atoms: electron affinity and ionization potential, as well as on (a feature related to) the first derivative of the electronic kinetic energy at the equilibrium distance.