Prediction of the photoelectrochemical performance of hematite electrodes using analytical data

Machine learning (ML) has been extensively utilized in various fields of chemistry, such as molecular design and optimization of the fabrication parameters of the material. However, there is still a difficulty in applying ML for devices/materials fabricated in a lab because plenty of data for accura...

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Veröffentlicht in:	Analyst (London) 2022-03, Vol.147 (7), p.1313-132
Hauptverfasser:	Nagai, Yuya, Katayama, Kenji
Format:	Artikel
Sprache:	eng
Schlagworte:	Data analysis Design optimization Electrodes Energy harvesting Feature extraction Hematite Machine learning Mathematical analysis Photoelectric effect Water splitting
Online-Zugang:	Volltext
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