001044683 001__ 1044683
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001044683 0247_ $$2doi$$a10.1002/aidi.202500022
001044683 037__ $$aFZJ-2025-03331
001044683 1001_ $$0P:(DE-Juel1)194716$$aZhang, Jiyun$$b0$$eCorresponding author
001044683 245__ $$aFeature Selection for Machine Learning‐Driven Accelerated Discovery and Optimization in Emerging Photovoltaics: A Review
001044683 260__ $$c2025
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001044683 500__ $$aMissing Journal: Advanced Intelligent Discovery (adv. intell. discov.) = 2943-9981 (import from CrossRef, Journals: juser.fz-juelich.de)
001044683 520__ $$aDeveloping reliable emerging photovoltaic (e-PV) technologies requires high-throughput material discovery, device design, and processing optimization. However, the effective process of the resulting high-dimensional, multivariate datasets remains a significant challenge. Integrating feature selection methods and machine learning (ML) provides a robust solution to reduce data dimensionality, improve predictive accuracy, and uncover material performance mechanisms. This review summarizes the advancements in synergizing feature selection methods, particularly the maximum relevance minimum redundancy (mRMR) method embedded, with Gaussian process regression (GPR) to advance e-PVs research. It highlights the importance of integrating feature selection with ML and high-throughput experimentation (HTE) frameworks to accelerate material screening, optimize manufacturing processes, and predict stability. Additionally, the review discusses key challenges such as data quality and model scalability and offers promising strategies to address these limitations. This data-driven approach offers a systematic pathway toward the accelerated discovery and optimization of e-PV technologies.
001044683 536__ $$0G:(DE-HGF)POF4-1212$$a1212 - Materials and Interfaces (POF4-121)$$cPOF4-121$$fPOF IV$$x0
001044683 536__ $$0G:(DE-HGF)POF4-1214$$a1214 - Modules, stability, performance and specific applications (POF4-121)$$cPOF4-121$$fPOF IV$$x1
001044683 588__ $$aDataset connected to CrossRef, Journals: juser.fz-juelich.de
001044683 7001_ $$0P:(DE-Juel1)204179$$aTan, Jiayi$$b1
001044683 7001_ $$aSong, Qizhen$$b2
001044683 7001_ $$0P:(DE-Juel1)200304$$aDU, Tian$$b3$$eCorresponding author$$ufzj
001044683 7001_ $$0P:(DE-Juel1)177626$$aHauch, Jens$$b4$$ufzj
001044683 7001_ $$0P:(DE-Juel1)176427$$aBrabec, Christoph$$b5$$eCorresponding author$$ufzj
001044683 773__ $$0PERI:(DE-600)0000000-0$$a10.1002/aidi.202500022$$gp. 202500022$$p202500022$$tMissing Journal / Fehlende Zeitschrift$$y2025
001044683 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)194716$$aForschungszentrum Jülich$$b0$$kFZJ
001044683 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)200304$$aForschungszentrum Jülich$$b3$$kFZJ
001044683 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)177626$$aForschungszentrum Jülich$$b4$$kFZJ
001044683 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)176427$$aForschungszentrum Jülich$$b5$$kFZJ
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001044683 9131_ $$0G:(DE-HGF)POF4-121$$1G:(DE-HGF)POF4-120$$2G:(DE-HGF)POF4-100$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-1214$$aDE-HGF$$bForschungsbereich Energie$$lMaterialien und Technologien für die Energiewende (MTET)$$vPhotovoltaik und Windenergie$$x1
001044683 920__ $$lyes
001044683 9201_ $$0I:(DE-Juel1)IET-2-20140314$$kIET-2$$lHelmholtz-Institut Erlangen-Nürnberg Erneuerbare Energien$$x0
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