Methods and applications of machine learning in computational design of optoelectronic semiconductors
Abstract The development of high-throughput computation and materials databases has laid the foundation for the emergence of data-driven machine learning methods in recent years. Machine learning has become a crucial methodology propelling researches in computational materials. It has demonstrated t...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Yang, Xiaoyu [verfasserIn] Zhou, Kun [verfasserIn] He, Xin [verfasserIn] Zhang, Lijun [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2024 |
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| Schlagwörter: |
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| Anmerkung: |
© Science China Press 2024 |
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| Übergeordnetes Werk: |
Enthalten in: Science China materials - Science China Press, 2014, 67(2024), 4 vom: 19. März, Seite 1042-1081 |
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| Übergeordnetes Werk: |
volume:67 ; year:2024 ; number:4 ; day:19 ; month:03 ; pages:1042-1081 |
| Links: |
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| DOI / URN: |
10.1007/s40843-024-2851-9 |
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SPR055564062 |
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| 520 | |a Abstract The development of high-throughput computation and materials databases has laid the foundation for the emergence of data-driven machine learning methods in recent years. Machine learning has become a crucial methodology propelling researches in computational materials. It has demonstrated tremendous potential in analyzing materials data, expediting materials calculations, predicting material properties, advancing the discovery, screening, and design of new materials. Consequently, an increasing number of methodologies, models, and frameworks of machine learning have emerged. This review provides a comprehensive overview of the latest advancements and applications of machine learning in computational design of optoelectronic semiconductors. We introduce the workflow and strategies of machine learning shallow models, ensemble models, and deep neural networks based on various material representation methods. The associated material databases and toolkits are also discussed. Furthermore, we delve into the applications of these models in predicting material stability, optoelectronic properties, materials inverse design, and establishing relationships between material structures and properties. Finally, we summarize and discuss the key challenges existing in current machine learning, with a specific focus on issues related to the size of available data, data quality, material representation, and materials inverse design. | ||
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10.1007/s40843-024-2851-9 doi (DE-627)SPR055564062 (SPR)s40843-024-2851-9-e DE-627 ger DE-627 rakwb eng 600 VZ Yang, Xiaoyu verfasserin aut Methods and applications of machine learning in computational design of optoelectronic semiconductors 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Science China Press 2024 Abstract The development of high-throughput computation and materials databases has laid the foundation for the emergence of data-driven machine learning methods in recent years. Machine learning has become a crucial methodology propelling researches in computational materials. It has demonstrated tremendous potential in analyzing materials data, expediting materials calculations, predicting material properties, advancing the discovery, screening, and design of new materials. Consequently, an increasing number of methodologies, models, and frameworks of machine learning have emerged. This review provides a comprehensive overview of the latest advancements and applications of machine learning in computational design of optoelectronic semiconductors. We introduce the workflow and strategies of machine learning shallow models, ensemble models, and deep neural networks based on various material representation methods. The associated material databases and toolkits are also discussed. Furthermore, we delve into the applications of these models in predicting material stability, optoelectronic properties, materials inverse design, and establishing relationships between material structures and properties. Finally, we summarize and discuss the key challenges existing in current machine learning, with a specific focus on issues related to the size of available data, data quality, material representation, and materials inverse design. machine learning (dpeaa)DE-He213 computational materials (dpeaa)DE-He213 optoelectronic semiconductor materials (dpeaa)DE-He213 Zhou, Kun verfasserin aut He, Xin verfasserin aut Zhang, Lijun verfasserin aut Enthalten in Science China materials Science China Press, 2014 67(2024), 4 vom: 19. März, Seite 1042-1081 (DE-627)815914733 (DE-600)2806677-7 2199-4501 nnns volume:67 year:2024 number:4 day:19 month:03 pages:1042-1081 https://dx.doi.org/10.1007/s40843-024-2851-9 X:VERLAG 0 lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 67 2024 4 19 03 1042-1081 |
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10.1007/s40843-024-2851-9 doi (DE-627)SPR055564062 (SPR)s40843-024-2851-9-e DE-627 ger DE-627 rakwb eng 600 VZ Yang, Xiaoyu verfasserin aut Methods and applications of machine learning in computational design of optoelectronic semiconductors 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Science China Press 2024 Abstract The development of high-throughput computation and materials databases has laid the foundation for the emergence of data-driven machine learning methods in recent years. Machine learning has become a crucial methodology propelling researches in computational materials. It has demonstrated tremendous potential in analyzing materials data, expediting materials calculations, predicting material properties, advancing the discovery, screening, and design of new materials. Consequently, an increasing number of methodologies, models, and frameworks of machine learning have emerged. This review provides a comprehensive overview of the latest advancements and applications of machine learning in computational design of optoelectronic semiconductors. We introduce the workflow and strategies of machine learning shallow models, ensemble models, and deep neural networks based on various material representation methods. The associated material databases and toolkits are also discussed. Furthermore, we delve into the applications of these models in predicting material stability, optoelectronic properties, materials inverse design, and establishing relationships between material structures and properties. Finally, we summarize and discuss the key challenges existing in current machine learning, with a specific focus on issues related to the size of available data, data quality, material representation, and materials inverse design. machine learning (dpeaa)DE-He213 computational materials (dpeaa)DE-He213 optoelectronic semiconductor materials (dpeaa)DE-He213 Zhou, Kun verfasserin aut He, Xin verfasserin aut Zhang, Lijun verfasserin aut Enthalten in Science China materials Science China Press, 2014 67(2024), 4 vom: 19. März, Seite 1042-1081 (DE-627)815914733 (DE-600)2806677-7 2199-4501 nnns volume:67 year:2024 number:4 day:19 month:03 pages:1042-1081 https://dx.doi.org/10.1007/s40843-024-2851-9 X:VERLAG 0 lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 67 2024 4 19 03 1042-1081 |
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10.1007/s40843-024-2851-9 doi (DE-627)SPR055564062 (SPR)s40843-024-2851-9-e DE-627 ger DE-627 rakwb eng 600 VZ Yang, Xiaoyu verfasserin aut Methods and applications of machine learning in computational design of optoelectronic semiconductors 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Science China Press 2024 Abstract The development of high-throughput computation and materials databases has laid the foundation for the emergence of data-driven machine learning methods in recent years. Machine learning has become a crucial methodology propelling researches in computational materials. It has demonstrated tremendous potential in analyzing materials data, expediting materials calculations, predicting material properties, advancing the discovery, screening, and design of new materials. Consequently, an increasing number of methodologies, models, and frameworks of machine learning have emerged. This review provides a comprehensive overview of the latest advancements and applications of machine learning in computational design of optoelectronic semiconductors. We introduce the workflow and strategies of machine learning shallow models, ensemble models, and deep neural networks based on various material representation methods. The associated material databases and toolkits are also discussed. Furthermore, we delve into the applications of these models in predicting material stability, optoelectronic properties, materials inverse design, and establishing relationships between material structures and properties. Finally, we summarize and discuss the key challenges existing in current machine learning, with a specific focus on issues related to the size of available data, data quality, material representation, and materials inverse design. machine learning (dpeaa)DE-He213 computational materials (dpeaa)DE-He213 optoelectronic semiconductor materials (dpeaa)DE-He213 Zhou, Kun verfasserin aut He, Xin verfasserin aut Zhang, Lijun verfasserin aut Enthalten in Science China materials Science China Press, 2014 67(2024), 4 vom: 19. März, Seite 1042-1081 (DE-627)815914733 (DE-600)2806677-7 2199-4501 nnns volume:67 year:2024 number:4 day:19 month:03 pages:1042-1081 https://dx.doi.org/10.1007/s40843-024-2851-9 X:VERLAG 0 lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 67 2024 4 19 03 1042-1081 |
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10.1007/s40843-024-2851-9 doi (DE-627)SPR055564062 (SPR)s40843-024-2851-9-e DE-627 ger DE-627 rakwb eng 600 VZ Yang, Xiaoyu verfasserin aut Methods and applications of machine learning in computational design of optoelectronic semiconductors 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Science China Press 2024 Abstract The development of high-throughput computation and materials databases has laid the foundation for the emergence of data-driven machine learning methods in recent years. Machine learning has become a crucial methodology propelling researches in computational materials. It has demonstrated tremendous potential in analyzing materials data, expediting materials calculations, predicting material properties, advancing the discovery, screening, and design of new materials. Consequently, an increasing number of methodologies, models, and frameworks of machine learning have emerged. This review provides a comprehensive overview of the latest advancements and applications of machine learning in computational design of optoelectronic semiconductors. We introduce the workflow and strategies of machine learning shallow models, ensemble models, and deep neural networks based on various material representation methods. The associated material databases and toolkits are also discussed. Furthermore, we delve into the applications of these models in predicting material stability, optoelectronic properties, materials inverse design, and establishing relationships between material structures and properties. Finally, we summarize and discuss the key challenges existing in current machine learning, with a specific focus on issues related to the size of available data, data quality, material representation, and materials inverse design. machine learning (dpeaa)DE-He213 computational materials (dpeaa)DE-He213 optoelectronic semiconductor materials (dpeaa)DE-He213 Zhou, Kun verfasserin aut He, Xin verfasserin aut Zhang, Lijun verfasserin aut Enthalten in Science China materials Science China Press, 2014 67(2024), 4 vom: 19. März, Seite 1042-1081 (DE-627)815914733 (DE-600)2806677-7 2199-4501 nnns volume:67 year:2024 number:4 day:19 month:03 pages:1042-1081 https://dx.doi.org/10.1007/s40843-024-2851-9 X:VERLAG 0 lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 67 2024 4 19 03 1042-1081 |
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10.1007/s40843-024-2851-9 doi (DE-627)SPR055564062 (SPR)s40843-024-2851-9-e DE-627 ger DE-627 rakwb eng 600 VZ Yang, Xiaoyu verfasserin aut Methods and applications of machine learning in computational design of optoelectronic semiconductors 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Science China Press 2024 Abstract The development of high-throughput computation and materials databases has laid the foundation for the emergence of data-driven machine learning methods in recent years. Machine learning has become a crucial methodology propelling researches in computational materials. It has demonstrated tremendous potential in analyzing materials data, expediting materials calculations, predicting material properties, advancing the discovery, screening, and design of new materials. Consequently, an increasing number of methodologies, models, and frameworks of machine learning have emerged. This review provides a comprehensive overview of the latest advancements and applications of machine learning in computational design of optoelectronic semiconductors. We introduce the workflow and strategies of machine learning shallow models, ensemble models, and deep neural networks based on various material representation methods. The associated material databases and toolkits are also discussed. Furthermore, we delve into the applications of these models in predicting material stability, optoelectronic properties, materials inverse design, and establishing relationships between material structures and properties. Finally, we summarize and discuss the key challenges existing in current machine learning, with a specific focus on issues related to the size of available data, data quality, material representation, and materials inverse design. machine learning (dpeaa)DE-He213 computational materials (dpeaa)DE-He213 optoelectronic semiconductor materials (dpeaa)DE-He213 Zhou, Kun verfasserin aut He, Xin verfasserin aut Zhang, Lijun verfasserin aut Enthalten in Science China materials Science China Press, 2014 67(2024), 4 vom: 19. März, Seite 1042-1081 (DE-627)815914733 (DE-600)2806677-7 2199-4501 nnns volume:67 year:2024 number:4 day:19 month:03 pages:1042-1081 https://dx.doi.org/10.1007/s40843-024-2851-9 X:VERLAG 0 lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 67 2024 4 19 03 1042-1081 |
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Yang, Xiaoyu |
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methods and applications of machine learning in computational design of optoelectronic semiconductors |
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Methods and applications of machine learning in computational design of optoelectronic semiconductors |
| abstract |
Abstract The development of high-throughput computation and materials databases has laid the foundation for the emergence of data-driven machine learning methods in recent years. Machine learning has become a crucial methodology propelling researches in computational materials. It has demonstrated tremendous potential in analyzing materials data, expediting materials calculations, predicting material properties, advancing the discovery, screening, and design of new materials. Consequently, an increasing number of methodologies, models, and frameworks of machine learning have emerged. This review provides a comprehensive overview of the latest advancements and applications of machine learning in computational design of optoelectronic semiconductors. We introduce the workflow and strategies of machine learning shallow models, ensemble models, and deep neural networks based on various material representation methods. The associated material databases and toolkits are also discussed. Furthermore, we delve into the applications of these models in predicting material stability, optoelectronic properties, materials inverse design, and establishing relationships between material structures and properties. Finally, we summarize and discuss the key challenges existing in current machine learning, with a specific focus on issues related to the size of available data, data quality, material representation, and materials inverse design. © Science China Press 2024 |
| abstractGer |
Abstract The development of high-throughput computation and materials databases has laid the foundation for the emergence of data-driven machine learning methods in recent years. Machine learning has become a crucial methodology propelling researches in computational materials. It has demonstrated tremendous potential in analyzing materials data, expediting materials calculations, predicting material properties, advancing the discovery, screening, and design of new materials. Consequently, an increasing number of methodologies, models, and frameworks of machine learning have emerged. This review provides a comprehensive overview of the latest advancements and applications of machine learning in computational design of optoelectronic semiconductors. We introduce the workflow and strategies of machine learning shallow models, ensemble models, and deep neural networks based on various material representation methods. The associated material databases and toolkits are also discussed. Furthermore, we delve into the applications of these models in predicting material stability, optoelectronic properties, materials inverse design, and establishing relationships between material structures and properties. Finally, we summarize and discuss the key challenges existing in current machine learning, with a specific focus on issues related to the size of available data, data quality, material representation, and materials inverse design. © Science China Press 2024 |
| abstract_unstemmed |
Abstract The development of high-throughput computation and materials databases has laid the foundation for the emergence of data-driven machine learning methods in recent years. Machine learning has become a crucial methodology propelling researches in computational materials. It has demonstrated tremendous potential in analyzing materials data, expediting materials calculations, predicting material properties, advancing the discovery, screening, and design of new materials. Consequently, an increasing number of methodologies, models, and frameworks of machine learning have emerged. This review provides a comprehensive overview of the latest advancements and applications of machine learning in computational design of optoelectronic semiconductors. We introduce the workflow and strategies of machine learning shallow models, ensemble models, and deep neural networks based on various material representation methods. The associated material databases and toolkits are also discussed. Furthermore, we delve into the applications of these models in predicting material stability, optoelectronic properties, materials inverse design, and establishing relationships between material structures and properties. Finally, we summarize and discuss the key challenges existing in current machine learning, with a specific focus on issues related to the size of available data, data quality, material representation, and materials inverse design. © Science China Press 2024 |
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Methods and applications of machine learning in computational design of optoelectronic semiconductors |
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https://dx.doi.org/10.1007/s40843-024-2851-9 |
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Zhou, Kun He, Xin Zhang, Lijun |
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| doi_str |
10.1007/s40843-024-2851-9 |
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2024-07-03T16:30:32.651Z |
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