We present a novel target-driven methodology devised
to predict
the Heyd–Scuseria–Ernzerhof (HSE) band gap of two-dimensional
(2D) materials leveraging the comprehensive C2DB database. This innovative
approach integrates machine learning and density functional theory
(DFT) calculations to predict the HSE band gap, conduction band minimum
(CBM), and valence band maximum (VBM) of 2176 types of 2D materials.
Subsequently, we collected a comprehensive data set comprising 3539
types of 2D materials, each characterized by its HSE band gaps, CBM,
and VBM. Considering the lattice disparities between MoSi2N4 (MSN) and 2D materials, our analysis predicted 766
potential MSN/2D heterostructures. These heterostructures are further
categorized into four distinct types based on the relative positions
of their CBM and VBM: Type I encompasses 230 variants, Type II comprises
244 configurations, Type III consists of 284 permutations, and 0 band
gap comprises 8 types.