Abstract:In this study, we undertake a Bayesian optimization of the Hubbard U parameters of wurtzite GaN and InN. The optimized Us are then tested within the Hubbard-corrected local density approximation (LDA+U) approach against standard density functional theory, as well as a hybrid functional (HSE06). We present the electronic band structures of wurtzite GaN, InN, and (1:1) InGaN superlattice. In addition, we demonstrate the outstanding performance of the new parametrization, when computing the internal electric-fiel… Show more
“…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.…”
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.
“…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.…”
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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