Identifying Direct Bandgap Silicon Structures with High-Throughput Search and Machine Learning Methods-Reference-Cited by-同舟云学术

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Identifying Direct Bandgap Silicon Structures with High-Throughput Search and Machine Learning Methods

Published:2024-07-24 Issue:30 Volume:128 Page:12677-12685
ISSN:1932-7447
Container-title:The Journal of Physical Chemistry C
language:en
Short-container-title:J. Phys. Chem. C

Author:

Wang Rui¹²^ORCID,Yu Hongyu¹²,Zhong Yang¹²,Xiang Hongjun¹²^ORCID

Affiliation:

1. Key Laboratory of Computational Physical Sciences (Ministry of Education), Institute of Computational Physical Sciences, State Key Laboratory of Surface Physics, and Department of Physics, Fudan University, Shanghai 200433, China

2. Shanghai Qi Zhi Institute, Shanghai 200030, China

Funder

National Key Research and Development Program of China

Basic and Applied Basic Research Foundation of Guangdong Province

Science and Technology Commission of Shanghai Municipality

National Natural Science Foundation of China

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jpcc.4c02967

Reference41 articles.

1. Status and perspectives of crystalline silicon photovoltaics in research and industry

2. Dopant‐Free Partial Rear Contacts Enabling 23% Silicon Solar Cells

3. Energy payback time (EPBT) and energy return on energy invested (EROI) of solar photovoltaic systems: A systematic review and meta-analysis

4. First-Principles Theory of Quasiparticles: Calculation of Band Gaps in Semiconductors and Insulators

5. Direct and quasi-direct band gap silicon allotropes with low energy and strong absorption in the visible for photovoltaic applications

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