2020
DOI: 10.1002/wcms.1489
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High‐throughput computational materials screening and discovery of optoelectronic semiconductors

Abstract: In the recent past, optoelectronic semiconductors have attracted significant research attention both experimentally and theoretically toward large-scale applications in energy conversion, lighting, imaging, detection, and so on. With advancement in computing power and rapid development of computational algorithms, scientific community resorts to materials simulation to explore the hidden potential behind thousands of potentially unknown materials within short timeframes that the real experiments might take a l… Show more

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Cited by 69 publications
(58 citation statements)
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References 172 publications
(329 reference statements)
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“…Zhang's group summarized the different screening criteria for the materials applied to various optoelectronic applications, including photovoltaic solar cells, photoelectrochemical cells, and LEDs. [ 223 ] These selected materials can be synthesized via the proper methods. The investigations combined with theoretical, computational, and experimental methods will tremendously promote the development of lead‐free perovskite materials.…”
Section: Discussionmentioning
confidence: 99%
“…Zhang's group summarized the different screening criteria for the materials applied to various optoelectronic applications, including photovoltaic solar cells, photoelectrochemical cells, and LEDs. [ 223 ] These selected materials can be synthesized via the proper methods. The investigations combined with theoretical, computational, and experimental methods will tremendously promote the development of lead‐free perovskite materials.…”
Section: Discussionmentioning
confidence: 99%
“…Simulation-based machine learning approaches have been applied to GaN-LED design optimization [ 126 , 127 ] but produced unreliable results [ 128 ]. The great popularity of such methods in materials science [ 129 ] and in photonics [ 130 ] seems hard to transfer to optoelectronic devices considering their complex internal physics and their material parameter uncertainties. In fact, the strength of machine learning lies in the analysis of large amounts of experimental data which are often routinely collected in the industrial LED production.…”
Section: Key Modeling and Simulation Challengesmentioning
confidence: 99%
“…Kuhar et al identified 74 materials with potential use as light absorbers in photovoltaic or photoelectrochemical devices from more than 20,000 materials using high-throughput computational approaches [30]. Moreover, this method can be used in other fields, such as semiconductor light-emitting diodes and transparent conducting materials [31]. In addition, novel materials design via machine learning has drawn much interest in many fields, such as photovoltaic materials [32], lithium-ion battery materials [33,34], and glass material system [35,36].…”
Section: Introductionmentioning
confidence: 99%