A machine-learned kinetic energy model for light weight metals and compounds of group III-V elements

TL;DR

This paper introduces a machine-learned kinetic energy model for orbital-free DFT tailored to light metals and group III-V compounds, achieving accurate energy predictions across various materials.

Contribution

The study develops a Gaussian process regression-based kinetic energy functional trained on diverse group III-V compounds, improving accuracy over traditional methods.

Findings

GPR model outperforms linear and polynomial regressions.

Unary compounds are crucial for effective training and extrapolation.

Model accurately reproduces energy-volume curves around equilibrium.

Abstract

We present a machine-learned (ML) model of kinetic energy for orbital-free density functional theory (OF-DFT) suitable for bulk light weight metals and compounds made of group III-V elements. The functional is machine-learned with Gaussian process regression (GPR) from data computed with Kohn-Sham DFT with plane wave bases and local pseudopotentials. The dataset includes multiple phases of unary, binary, and ternary compounds containing Li, Al, Mg, Si, As, Ga, Sb, Na, Sn, P, and In. A total of 433 materials were used for training, and 18 strained structures were used for each material. Averaged (over the unit cell) kinetic energy density is fitted as a function of averaged terms of the 4th order gradient expansion and the product of the density and effective potential. The kinetic energy predicted by the model allows reproducing energy-volume curves around equilibrium geometry with good accuracy. We show that the GPR model beats linear and polynomial regressions. We also find that unary compounds sample a wider region of the descriptor space than binary and ternary compounds, and it is therefore important to include them in the training set; a GPR model trained on a small number of unary compounds is able to extrapolate relatively well to binary and ternary compounds but not vice versa.