Machine learning band gaps from the electron density

TL;DR

This paper demonstrates that machine learning models can accurately predict the band gaps of semiconductors from electron densities, offering a computationally efficient alternative to traditional methods.

Contribution

The authors introduce a modified Behler-Parrinello architecture that enhances model capacity while preserving symmetry, enabling accurate band gap predictions from density functional theory data.

Findings

Achieved band gap predictions comparable to hybrid functionals.

Reduced computational cost for band gap estimation.

Proposed a symmetry-preserving neural network architecture.

Abstract

A remarkable consequence of the Hohenberg-Kohn theorem of density functional theory is the existence of an injective map between the electronic density and any observable of the many electron problem in an external potential. In this work, we study the problem of predicting a particular observable, the band gap of semiconductors and band insulators, from the knowledge of the local electronic density. Using state-of-the-art machine learning techniques, we predict the experimental band gaps from computationally inexpensive density functional theory calculations. We propose a modified Behler-Parrinello (BP) architecture that greatly improves the model capacity while maintaining the symmetry properties of the BP architecture. Using this scheme, we obtain band gaps at a level of accuracy comparable to those obtained with state of the art and computationally intensive hybrid functionals, thus significantly reducing the computational cost of the task.