A band-gap database for semiconducting inorganic materials calculated with hybrid functional

Abstract

Semiconducting inorganic materials with band gaps ranging between 0 and 5 eV constitute major components in electronic, optoelectronic and photovoltaic devices. Since the band gap is a primary material property that affects the device performance, large band-gap databases are useful in selecting optimal materials in each application. While there exist several band-gap databases that are theoretically compiled by density-functional-theory calculations, they suffer from computational limitations such as band-gap underestimation and metastable magnetism. In this data descriptor, we present a computational database of band gaps for 10,481 materials compiled by applying a hybrid functional and considering the stable magnetic ordering. For benchmark materials, the root-mean-square error in reference to experimental data is 0.36 eV, significantly smaller than 0.75–1.05 eV in the existing databases. Furthermore, we identify many small-gap materials that are misclassified as metals in other databases. By providing accurate band gaps, the present database will be useful in screening materials in diverse applications.

A universal similarity based approach for predictive uncertainty quantification in materials science

“…Wang et al 77 implemented three models to predict the HSE bandgap of inorganic crystals from the SNUMAT database 56 . These models were trained using information from the constituent elements, PBE bandgap, and the combination of inputs from the first two models, respectively.…”

“…For this study, 10,434 inorganic crystal structures and their corresponding bandgap values were obtained from the computational database presented by Kim et al 56 Initially, the band edges were identified within the generalized gradient approximation, as implemented by Perdew, Burke, and Ernzerhof (PBE) 57 . Then, one-shot calculations with the screened hybrid functional of Heyd, Scuseria, and Ernzerhof (HSE06) 58 were conducted to estimate the bandgap values at the k points of the band edges found with the PBE functional.…”

High-Throughput Predictions of Metal–Organic Framework Electronic Properties: Theoretical Challenges, Graph Neural Networks, and Data Exploration

“…6 Another approach is to carry out higher-accuracy DFT calculations on a subset of materials and use them to train an ML model that can make more reliable predictions. Recently, large datasets of band gaps computed with meta-GGA and hybrid functionals have been published for inorganic solids, [35][36][37] although no such resource currently exists for MOFs.…”

Machine Learning Prediction of the Exfoliation Energies of Two-Dimension Materials via Data-Driven Approach

“…Notably, a rather large materials data set has been constructed on the basis of the accumulation of extensive research during the past years, − which shows new ways for the screening of novel 2D materials using machine learning (ML) methods. , ML methods are now widely used to predict the band gap, enthalpy of formation, transition-state properties, and other properties of materials and molecules. − A similar approach was employed in 2D material studies. Saito et al.…”