Machine Learning（ML）-Assisted Design and Fabrication for Solar Cells

论文摘要

Photovoltaic (PV) technologies have attracted great interest due to their capability of generating electricity directly from sunlight. Machine learning(ML) is a technique for computer to learn how to perform a specific task using known data. It can be used in many areas and has become a hot research topic recently due to the rapid accumulation of data and advancement of computer hardware. The application of ML techniques in the design and fabrication of solar cells started slowly but has recently gained tremendous momentum. An exhaustive compilation of the literatures indicates that all the major aspects in the research and development of solar cells can be effectively assisted by ML techniques. If combined with other tools and fed with additional theoretical and experimental data, more accurate and robust results can be achieved from ML techniques. The aspects can be grouped into four categories:prediction of material properties,optimization of device structures, optimization of fabrication processes, and reconstruction of measurement data. A statistical analysis of the literatures shows that artificial neural network (ANN) and genetic algorithm (GA) are the two most applied ML techniques and the topics in the optimization of device structures and optimization of fabrication processes are more popular.This article can be used as a reference by all PV researchers who are interested in ML techniques.

论文目录

文章来源

类型: 期刊论文

作者: Fan Li,Xiaoqi Peng,Zuo Wang,Yi Zhou,Yuxia Wu,Minlin Jiang,Min Xu

来源: 能源与环境材料(英文) 2019年04期

年度: 2019

分类: 工程科技Ⅰ辑,工程科技Ⅱ辑,信息科技

专业: 电力工业,自动化技术

单位: Institute of Advanced Study, Nanchang University,School of Information Engineering, Nanchang University,Department of Computational Biology, Carnegie Mellon University

基金: partialy supported by Nanchang University,under Research Grant 9166-2701010119

分类号: TM914.4;TP181

页码: 280-291

总页数: 12

文件大小: 565K

下载量: 4

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