$Predicting the formation of fractionally doped perovskite oxides by a function-confined machine learning method$

Predicting the formation of fractionally doped perovskite oxides by a function-confined machine learning method

Fractionally doped perovskites oxides (FDPOs) have demonstrated ubiquitous applications such as energy conversion, storage and harvesting, catalysis, sensor, superconductor, ferroelectric, piezoelectric, magnetic, and luminescence. Hence, an accurate, cost-effective, and easy-to-use methodology to d...

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Bibliographic Details
Main Authors:	Ding, F. (Author), Luo, F. (Author), Santomauro, A. (Author), Tong, J. (Author), Zhai, X. (Author), Zhao, Z. (Author)
Format:	Article
Language:	English
Published:	Springer Nature 2022
Subjects:	Catalysis sensors Cost effective Cost effectiveness Digital storage Forecasting Hydrogen production Hydrogen storage Machine learning Machine learning methods Machine-learning Perovskite Perovskite oxides Piezoelectric Prediction accuracy Solar energy Solar power generation Solar thermo-chemical hydrogen Thermochemical hydrogen production Ubiquitous application
Online Access:	View Fulltext in Publisher

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Predicting the formation of fractionally doped perovskite oxides by a function-confined machine learning method

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