Formation Energy Prediction of Doped Perovskite Structures Based on Transfer Learning with Small Datasets

Doped perovskites are widely studied in the domain of perovskite material design. However, due to the limited data available for the target materials, machine learning methods based on small datasets become particularly important. In this study, we propose a transfer learning strategy aimed at predicting doped perovskites on limited data samples. This strategy first utilizes the ABO3-type perovskite dataset to develop a deep learning source model based on its formation energies. Then, fine-tuning is performed on the doped perovskite structure dataset to obtain a model with good transferability, applicable to the doped perovskite oxide target domain. Based on the transfer learning model, we further predict the formation energies of 12,897 A2BB′O6 compounds, 10,401 AA′B2O6 compounds, and 49,723 AA′BB′O6 compounds. With the tolerance factor t ∈ [0.7–1.1], octahedral factor μ ∈ [0.45–0.7], and the modified tolerance factor τ ∈ [0, 4.18] for screening, we successfully predict 3389 A2B′BO6, 3002 AA′B2O6, and 13,563 AA′BB′O6 structures as potential stable doped perovskite candidates. Among these filtered results, 821 A2B′BO6, 69 AA′B2O6, and 6 AA′BB′O6 compounds have been reported in the OQMD database. For each doped perovskite, we select the candidate with the lowest formation energy and perform DFT validation. This resulted in three newly reported stable doped perovskite materials: CaSrHfScO6, BaSrHf2O6, and Ba2HfNdO6. The transfer learning-based perovskite material design method proposed in this study not only effectively addresses the challenges of model training on small datasets but also significantly improves the accuracy and stability of doped perovskite material predictions. Through transfer learning, the model can fully leverage the data and knowledge from the ABO3-type perovskite, effectively overcoming the problem of limited data. This strategy provides a new approach for efficient perovskite material design, enabling broader structural and performance predictions under limited experimental data conditions, and offering a powerful tool for the development of novel functional materials.