Efficient Sampling for Machine Learning Electron Density and Its Response in Real Space

TL;DR

This paper introduces an efficient sampling strategy combined with a neural network model to accurately predict electron density and its response to electric fields using fewer training points, enabling detailed charge and electrostatic analyses.

Contribution

It proposes a novel grid-point sampling method integrated with a neural network to reduce data requirements for electron density modeling in real space.

Findings

Achieved accurate electron density predictions with fewer training points.

Enabled analysis of charge transfer and electrostatic potential changes.

Demonstrated effectiveness on molecular and electrode systems.

Abstract

Electron density is a fundamental quantity, which can in principle determine all ground state electronic properties of a given system. Although machine learning (ML) models for electron density based on either an atom-centered basis or a real-space grid have been proposed, the demand for the number of high-order basis functions or grid points is enormous. In this work, we propose an efficient grid-point sampling strategy that combines a targeted sampling favoring large density and a screening of grid points associated with linearly independent atomic features. This new sampling strategy is integrated with a field-induced recursively embedded atom neural network model to develop a real-space grid-based ML model for electron density and its response to an electric field. This approach is applied to a QM9 molecular dataset, a H2O/Pt(111) interfacial system, and an Au(100) electrode under an electric field. The number of training points is found much smaller than previous models, when yielding comparably accurate predictions for the electron density of the entire grid. The resultant machine learned electron density model enables us to properly partition partial charge onto each atom and analyze the charge variation upon proton transfer in the H2O/Pt(111) system. The machined learned electronic response model allows us to predict charge transfer and the electrostatic potential change induced by an electric field in an Au(100) electrode.