How Machine Learning Predicts and Explains the Performance of Perovskite Solar Cells-Reference-Cited by-同舟云学术

How Machine Learning Predicts and Explains the Performance of Perovskite Solar Cells

Published:2022-03-07 Issue:6 Volume:6 Page:2101100
ISSN:2367-198X
Container-title:Solar RRL
language:en
Short-container-title:Solar RRL

Author:

Liu Yiming¹^ORCID,Yan Wensheng¹²,Han Shichuang³,Zhu Heng¹,Tu Yiteng¹,Guan Li³,Tan Xinyu¹

Affiliation:

1. College of Electrical Engineering & New Energy Hubei Provincial Collaborative Innovation Center for New Energy Microgrid China Three Gorges University Yichang 443002 China

2. Electronics and Information College Hangzhou Dianzi University Hangzhou 310018 China

3. Department of Physics Science and Technology Hebei University Baoding 071000 China

Funder

National Natural Science Foundation of China

Higher Education Discipline Innovation Project

Publisher

Wiley

Subject

Electrical and Electronic Engineering,Energy Engineering and Power Technology,Atomic and Molecular Physics, and Optics,Electronic, Optical and Magnetic Materials

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/solr.202101100

Reference39 articles.

1. National Renewable Energy Lab Best research-cell efficiency chart. Photovoltaic Research NREL https://www.nrel.gov/pv/cell-efficiency.html(accessed: August 2021).

2. High-performance dopant-free conjugated small molecule-based hole-transport materials for perovskite solar cells

3. Perovskite Materials for Light-Emitting Diodes and Lasers

4. Mixed-dimensional self-assembly organic–inorganic perovskite microcrystals for stable and efficient photodetectors

5. Understanding the physical properties of hybrid perovskites for photovoltaic applications

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