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

• 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

 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 discover new compositions is much needed. Here, we developed a function-confined machine learning methodology to discover new FDPOs with high prediction accuracy from limited experimental data. By focusing on a specific application, namely solar thermochemical hydrogen production, we collected 632 training data and defined 21 desirable features. Our gradient boosting classifier model achieved a high prediction accuracy of 95.4% and a high F1 score of 0.921. Furthermore, when verified on additional 36 experimental data from existing literature, the model showed a prediction accuracy of 94.4%. With the help of this machine learning approach, we identified and synthesized 11 new FDPO compositions, 7 of which are relevant for solar thermochemical hydrogen production. We believe this confined machine learning methodology can be used to discover, from limited data, FDPOs with other specific application purposes. © 2022, The Author(s).