Bayesian Optimization of Hubbard U’s for Investigating InGaN Superlattices

Popov, Maxim N.; Spitaler, Jürgen; Romaner, Lorenz; Bedoya-Martínez, Natalia; Hammer, René

doi:10.3390/electronicmat2030025

Cited by 1 publication

(1 citation statement)

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“…Recently, the machine learning (ML) method was employed, specifically the Bayesian optimization (BO), to extract the U value according to higher-level ab initio results [13]. Such a method has been also successfully applied to interface [14,15] and superlattice [16], with extra computational overhead.…”

Section: Introductionmentioning

confidence: 99%

Predicting structure-dependent Hubbard U parameters via machine learning

Cai,

Cao,

Xie

et al. 2024

Mater. Futures

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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 semi-local approximations can be corrected without much computational overhead. However, finding appropriate U parameters for a given system and structure is non-trivial and computationally intensive, because the U value has generally a strong chemical and structural dependence. In this work, we address this issue by building a machine learning (ML) model that enables the prediction of material- and structure-specific U values at nearly no computational cost. Using Mn-O as an example, the ML model is trained by calibrating DFT+U electronic structures with the hybrid functional results of more than 3000 structures. The model allows us to determine an accurate U value (MAE=0.128 eV, R2=0.97) for any given structure. Further analysis reveals that M-O bond lengths are key local structural properties in determining the U value. This approach of the ML U model is universally applicable, to significantly expand and solidify the use of the DFT+U method.

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