Predicting structure-dependent Hubbard U parameters for assessing hybrid functional-level exchange via machine learning

TL;DR

This paper develops a machine learning model to predict material-specific Hubbard U parameters from structural data, enabling efficient and accurate DFT+U calculations for correlated materials without extensive first-principles computations.

Contribution

The study introduces a ML approach to predict U parameters based solely on structure, significantly reducing computational effort while maintaining accuracy comparable to traditional methods.

Findings

ML model achieves MAE=0.128 eV and R2=0.97 in U prediction

U values are primarily determined by local chemical structure and bond lengths

The approach is universally applicable to simplify DFT+U calculations

Abstract

DFT+U is a widely used treatment in the density functional theory (DFT) to deal with correlated materials that contain open-shell elements, whereby the quantitative and sometimes even qualitative failures of local and semilocal approximations can be corrected without much computational overhead. However, finding appropriate U parameters for a given system is non-trivial and usually requires computationally intensive and cumbersome first-principles calculations. In this Letter, we address this issue by building a machine learning (ML) model to predict material-specific U parameters only from the structural information. An ML model is trained for the Mn-O chemical system by calibrating their DFT+U electronic structures with the hybrid functional results of more than Mn-O 3000 structures. The model allows us to determine a reliable U value (MAE=0.128 eV, R2=0.97) for any given structure at nearly no computational cost; yet the obtained U value is as good as that obtained from the conventional first-principles methods. Further analysis reveals that the U value is primarily determined by the local chemical structure, especially the bond lengths, and this property is well captured by the ML model developed in this work. This concept of the ML U model is universally applicable and can considerably ease the usage of the DFT+U method by providing structure-specific, readily accessible U values.