Machine Learning for Understanding Compatibility of Organic–Inorganic Hybrid Perovskites with Post-Treatment Amines-Reference-Cited by-同舟云学术

Machine Learning for Understanding Compatibility of Organic–Inorganic Hybrid Perovskites with Post-Treatment Amines

Published:2019-01-04 Issue:2 Volume:4 Page:397-404
ISSN:2380-8195
Container-title:ACS Energy Letters
language:en
Short-container-title:ACS Energy Lett.

Author:

Yu Yongze¹^ORCID,Tan Xuanheng²,Ning Shougui¹³,Wu Yiying¹^ORCID

Affiliation:

1. Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio 43210, United States

2. Department of Chemical Physics, School of Chemistry and Material Science, University of Science and Technology of China, Hefei, Anhui 230022, China

3. Institute of Laser & Micro/Nano Engineering, College of Electronics & Information Engineering, Sichuan University, Chengdu 610064, China

Funder

U.S. Department of Energy

Publisher

American Chemical Society (ACS)

Subject

Materials Chemistry,Energy Engineering and Power Technology,Fuel Technology,Renewable Energy, Sustainability and the Environment,Chemistry (miscellaneous)

Link

https://pubs.acs.org/doi/pdf/10.1021/acsenergylett.8b02451

Reference58 articles.

1. Promises and challenges of perovskite solar cells

2. A solvent- and vacuum-free route to large-area perovskite films for efficient solar modules

3. Efficient planar heterojunction perovskite solar cells by vapour deposition

4. Compositional engineering of perovskite materials for high-performance solar cells

5. High-efficiency two-dimensional Ruddlesden–Popper perovskite solar cells

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