Applications and potentials of machine learning in optoelectronic materials research: An overview and perspectives

doi:10.1088/1674-1056/ad01a4

Abstract Optoelectronic materials are essential for today's scientific and technological development, and machine learning provides new ideas and tools for their research. In this paper, we first summarize the development history of optoelectronic materials and how materials informatics drives the innovation and progress of optoelectronic materials and devices. Then, we introduce the development of machine learning and its general process in optoelectronic materials and describe the specific implementation methods. We focus on the cases of machine learning in several application scenarios of optoelectronic materials and devices, including the methods related to crystal structure, properties (defects, electronic structure) research, materials and devices optimization, material characterization, and process optimization. In summarizing the algorithms and feature representations used in different studies, it is noted that prior knowledge can improve optoelectronic materials design, research, and decision-making processes. Finally, the prospect of machine learning applications in optoelectronic materials is discussed, along with current challenges and future directions. This paper comprehensively describes the application value of machine learning in optoelectronic materials research and aims to provide reference and guidance for the continuous development of this field.

Keywords: optoelectronic materials devices machine learning prior knowledge

Received: 07 August 2023
Revised: 02 October 2023
Accepted manuscript online: 10 October 2023

PACS:	61.72.-y	(Defects and impurities in crystals; microstructure)
	07.05.Mh	(Neural networks, fuzzy logic, artificial intelligence)
	85.60.-q	(Optoelectronic devices)
	71.20.Nr	(Semiconductor compounds)

Fund: Project supported by the National Natural Science Foundation of China (Grant No.61601198) and the University of Jinan PhD Foundation (Grant No.XBS1714).

Corresponding Authors: Xiao-Qian Fu
E-mail: ise_fuxq@ujn.edu.cn

Cite this article:

Cheng-Zhou Zhang(张城洲) and Xiao-Qian Fu(付小倩) Applications and potentials of machine learning in optoelectronic materials research: An overview and perspectives 2023 Chin. Phys. B 32 126103

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