2024
DOI: 10.1021/acs.jpclett.4c01013
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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
et al.

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 characteri… Show more

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Cited by 10 publications
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References 37 publications
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