Insight into the Electronic Properties of Semiconductor Heterostructure Based on Machine Learning and First-Principles-Reference-Cited by-同舟云学术

Insight into the Electronic Properties of Semiconductor Heterostructure Based on Machine Learning and First-Principles

Published:2023-02-24 Issue:9 Volume:15 Page:12462-12472
ISSN:1944-8244
Container-title:ACS Applied Materials & Interfaces
language:en
Short-container-title:ACS Appl. Mater. Interfaces

Author:

Yuan Yuanyuan¹,Ren Junqiang¹,Xue Hongtao¹,Li Junchen¹,Tang Fuling¹^ORCID,La Peiqing¹,Lu Xuefeng¹^ORCID

Affiliation:

1. State Key Laboratory of Advanced Processing and Recycling of Non-ferrous Metal, Department of Materials Science and Engineering, Lanzhou University of Technology, Lanzhou 730050, PR China

Funder

Gansu Education Department

National Natural Science Foundation of China

Publisher

American Chemical Society (ACS)

Subject

General Materials Science

Link

https://pubs.acs.org/doi/pdf/10.1021/acsami.2c15957

Reference45 articles.

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2. Jain, A.; Ong, S. P.; Hautier, G.; Chen, W.; Richards, W. D.; Dacek, S.; Cholia, S.; Gunter, D.; Skinner, D.; Ceder, G.; Persson, K. The Materials Project, https://materialsproject.org/.

3. The inorganic crystal structure data base

4. The Cambridge crystallographic data base

5. Direct Synthesis of Large-Area 2D Mo2C on In Situ Grown Graphene

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