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Combined Machine Learning and High-Throughput Calculations Predict Heyd-Scuseria-Ernzerhof Band Gap of 2D Materials and Potential MoSi 2 N 4 Heterostructures.

Autor: Zhang W; 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., Guo J; 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., Lv X; 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., Zhang F; College of Physics and Electronic Information, Yan'an University, Yan'an 716000, P. R. China.

Publikováno v: The journal of physical chemistry letters [J Phys Chem Lett] 2024 May 23; Vol. 15 (20), pp. 5413-5419. Date of Electronic Publication: 2024 May 14.