Combined Machine Learning and High-Throughput Calculations Predict Heyd-Scuseria-Ernzerhof Band Gap of 2D Materials and Potential MoSi2N4 Heterostructures

Weibin Zhang; Jie Guo; Xiankui Lv; Fuchun Zhang

doi:10.1021/acs.jpclett.4c01013

Combined Machine Learning and High-Throughput Calculations Predict Heyd-Scuseria-Ernzerhof Band Gap of 2D Materials and Potential MoSi₂N₄ Heterostructures

J Phys Chem Lett. 2024 May 23;15(20):5413-5419. doi: 10.1021/acs.jpclett.4c01013. Epub 2024 May 14.

Authors

Weibin Zhang¹, Jie Guo¹, Xiankui Lv¹, Fuchun Zhang²

Affiliations

¹ College of Physics and Electronics Information, Yunnan Key Laboratory of Optoelectronic Information Technology, Key Laboratory of Advanced Technique & Preparation for Renewable Energy Materials-Ministry of Education, Yunnan Normal University, Kunming 650500, P.R. China.
² College of Physics and Electronic Information, Yan'an University, Yan'an 716000, P. R. China.

PMID: 38743311
DOI: 10.1021/acs.jpclett.4c01013

Abstract

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.