Prediction of ABX3 Perovskite Formation Energy Using Machine Learning

Prediction of ABX3 Perovskite Formation Energy Using Machine Learning

Materials

10.3390/ma18132927

https://doi.org/10.3390/ma18132927

Ziliang Deng, Kailing Fang, Chong Guo, Zhichao Gong, Haojie Yue, Huacheng Zhang, Kang Li, Kun Guo, Zhiyong Liu, Bing Xie, Jinshan Lu, Kui Yao, Francis Eng Hock Tay

ABX3 perovskites; perovskite solar cells; formation energy; machine learning

20 June 2025

Deng, Z., Fang, K., Guo, C., Gong, Z., Yue, H., Zhang, H., Li, K., Guo, K., Liu, Z., Xie, B., Lu, J., Yao, K., & Tay, F. E. H. (2025). Prediction of ABX3 Perovskite Formation Energy Using Machine Learning. Materials, 18(13), 2927. https://doi.org/10.3390/ma18132927

Materials with perovskite phases are widely used in solar cells and ferroelectric, piezoelectric, dielectric and superconducting devices due to their various notable functions. However, structural instability limits some compositions in forming robust perovskite phases for device applications. The analytical approach using the tolerance factor (t) can only guarantee prediction accuracy within a limited range, ascribed to its nature of overlooking the atomic interaction. Hence, here we establish a prediction model using formation energy as the target parameter for its reflection of the reaction of atoms and apply machine learning as the analysis method since it has been successfully employed in plenty of material property prediction studies. Machine learning employs statistical methodologies to identify correlative patterns within large-scale datasets, enabling accurate predictions with robust generalization. In this work, we built a model to predict the formation energy of ABX3 perovskite using machine learning and achieved a model with an R-squared value of 0.928 and a root mean square error of 0.301 eV/atom, validated by first-principles computations. In total, 75% of the values were correctly predicted within an error lower than 0.06. This work could contribute to accelerating the study of solving perovskites’ instability.

Attribution 4.0 International (CC BY 4.0)

This research / project is supported by the A*STAR - Advanced Manufacturing and Engineering (AME) Programmatic Fund
Grant Reference no. : A20G9b0135

This research / project is supported by the A*STAR - Industry Alignment Fund – Industry Collaboration Projects
Grant Reference no. : I2301E0027

Published source must be acknowledged with a citation Must link to publisher version with DOI:10.3390/ma18132927

https://oar.a-star.edu.sg/communities-collections/articles/22790

1996-1944

Institute of Materials Research and Engineering