Combined Machine Learning and High-Throughput Calculations Predict Heyd–Scuseria–Ernzerhof Band Gap of 2D Materials and Potential MoSi<sub>2</sub>N<sub>4</sub> Heterostructures-Reference-Cited by-同舟云学术

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

Published:2024-05-14 Issue:20 Volume:15 Page:5413-5419
ISSN:1948-7185
Container-title:The Journal of Physical Chemistry Letters
language:en
Short-container-title:J. Phys. Chem. Lett.

Author:

Zhang Weibin¹^ORCID,Guo Jie¹,Lv Xiankui¹,Zhang Fuchun²^ORCID

Affiliation:

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

Funder

National Natural Science Foundation of China

Yunnan Fundamental Research Projects

Yunnan Revitalization Talent Support Program

Spring City Plan: the High-level Talent Promotion and Training Project of Kunming

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jpclett.4c01013

Reference37 articles.

1. Electric Field Effect in Atomically Thin Carbon Films