Beyond molecular structure: critically assessing machine learning for designing organic photovoltaic materials and devices

Our study explores the current state of machine learning (ML) as applied to predicting and designing organic photovoltaic (OPV) devices. We outline key considerations for selecting the method of encoding a molecular structure and selecting the algorithm while also emphasizing important aspects of tr...

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Veröffentlicht in:	Journal of materials chemistry. A, Materials for energy and sustainability Materials for energy and sustainability, 2024-06, Vol.12 (24), p.1454-14558
Hauptverfasser:	Seifrid, Martin, Lo, Stanley, Choi, Dylan G, Tom, Gary, Le, My Linh, Li, Kunyu, Sankar, Rahul, Vuong, Hoai-Thanh, Wakidi, Hiba, Yi, Ahra, Zhu, Ziyue, Schopp, Nora, Peng, Aaron, Luginbuhl, Benjamin R, Nguyen, Thuc-Quyen, Aspuru-Guzik, Alán
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Data mining Datasets Fabrication Learning algorithms Machine learning Molecular structure Performance prediction Photovoltaics
Online-Zugang:	Volltext
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