Efficient, Equivariant Predictions of Distributed Charge Models

TL;DR

This paper introduces DCM-net, an equivariant neural network that efficiently models molecular electrostatic potentials with high accuracy, capturing anisotropy and conformational dependence better than traditional point charge methods.

Contribution

The paper presents a novel equivariant neural network architecture for constructing distributed charge models that surpass traditional methods in accuracy and physical relevance.

Findings

Two-charge-per-atom models achieve accuracy comparable to atomic dipoles.

Three- and four-charge-per-atom models match atomistic multipole expansions.

Transfer learning improves ESP and dipole moment predictions for unseen molecules.

Abstract

A machine learning (ML) based equivariant neural network for constructing distributed charge models (DCMs) of arbitrary resolution, DCM-net, is presented. DCMs efficiently and accurately model the anisotropy of the molecular electrostatic potential (ESP) and go beyond the point charge representation used in conventional molecular mechanics (MM) energy functions. This is particularly relevant for capturing the conformational dependence of the ESP (internal polarization) and chemically relevant features such as lone pairs or {\sigma}-holes. Across conformational space, the learned charge positions from DCM-net are stable and continuous. Across the QM9 chemical space, two-charge-per-atom models achieve accuracies comparable to fitted atomic dipoles for previously unseen molecules (0.75 (kcal/mol)/e). Three- and four-charge-per-atom models reach accuracies competitive with atomistic multipole expansions up to quadrupole level (0.55 (kcal/mol)/e). Pronounced improvements of the ESP are found around O and F atoms, both of which are known to feature strongly anisotropic fields, and for aromatic systems. Across the QM9 reference data set, molecular dipole moments improve by 0.1 D compared with fitted monopoles. Transfer learning on dipeptides yields a 0.2 (kcal/mol)/e ESP improvement for unseen samples and a two-fold MAE reduction for molecular dipole moments versus fitted monopoles. Overall, DCM-net offers a fast and physically meaningful approach to generating distributed charge models for running pure ML or mixed ML/MM based molecular simulations. level (0.55 (kcal/mol)/e).