Authors: Luiz Gustavo Zandavalli | Marcos V. Barp | Sebastián Vecino‐Mantilla | Fabiana Matos de Oliveira | Zahreddine Hafsi | Massimiliano Lo Faro
Source: ECS Meeting Abstracts (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

A Machine Learning Approach to Predicting the Lattice Constant of Simple Perovskite Oxides As Oxygen Electrodes for Solid Oxide Electrochemical Cells Since the discovery of perovskite oxides, they have been extensively researched and developed for a variety of applications, for instance energy storage and conversion, fuel cells, solar cells, among many others. They can come in different configurations, for example double perovskites (AA’BB’O 6 ), hybrid organic-inorganic perovskites and simple ABO 3 perovskites. The crystal structure plays a critical role in determining the properties of perovskite oxides. It is defined by the lattice parameters and the precise arrangement of atomic positions within the unit cell [1]. Understanding the crystal structure is essential for analyzing the stability of the material, which is particularly important for different applications, especially in solid oxide fuel cells (SOFCs) and solid oxide electrochemical cells (SOECs). Usually, the crystal structure can be determined by using either experimental or theoretical methods. Regarding the experimental methods, one of the most common is the use of X-ray diffraction, however, that would require the material to be synthesized, which is a costly process, especially when considering rare earth metals and critical materials. As for the theoretical methods, the most accurate ones for performing this task are the first-principle methods which can be computationally demanding. Alternatively, with the recent advances in artificial intelligence and machine learning, it is now possible to develop simpler and much faster models to overcome the aforementioned challenges [2]. In this work, we develop machine learning models to predict lattice constants of simple perovskite oxides, using as features: the Ionic Radius, Ionic Charge, Electronegativity and Valence of the A and B site elements. The tabular dataset used was obtained from public databases and other researches [3-5]. The proposed models were trained and tested using lattice constants of cubic and semi-cubic perovskites experimentally and theoretically obtained by works previously reported in literature [6-11]. The main focus of this study is on perovskites with A 2+ B 4+ O 3 and A 3+ B 3+ O 3 configurations. The selected models for this study were three different tree-based models, being Validation Tree, Random Forest, and Random Forest using gradient boosted trees through the XGBoost Python package. These models were chosen due to the efficiency of tree-based models when using tabular data and their low computational costs. The models evaluation and accuracy were measured using a few different metrics such as Root Mean Square Deviation (RMSE), F1 score, among others. The models proved to be efficient in predicting the lattice constant of perovskites even when using a small amount of features that can be very easily obtained. This work represents a first step in simplifying the calculations for obtaining viable new possible perovskite oxides as oxygen electrodes for SOFCs and SOECs and has the potential to be greatly expanded and applied in further studies regarding the subject. Acknowledgments Dr. Zandavalli thanks the European Union – NextGeneration EU from the Italian Ministry of Environment and Energy Security POR H2 AdP MMES/ENEA with involvement of CNR and RSE, PNRR - Mission 2, Component 2, Investment 3.5 "Ricerca e sviluppo sull’idrogeno", CUP: B93C22000630006. In addition, the authors acknowledge the MUR for funding the PRIN project SUPERH2, CUPE53D23009400006, PRIN PNRR project OxCellenT, CUP B53D23027440001, and ITELECTROLAB, the Joint lab between CNR-ITAE and IQSC-USP financed by the National Research Council of Italy (CNR) References 1 Zhang, Y., Xu, X., Helyion, 7 (2021), e07601, Modeling of Lattice Parameters of Cubic Perovskite Oxides and Halides. 2 Li, Y., Yang, W., Dong, R., Hu, J., ACS Omega, 7, 17(2021) 11585-11594, Mlatticeabc: Generic Lattice Constant Prediction of Crystal Materials Using Machine Learning. 3 Shannon, R., Acta Cryst, 32, 1 (1976) 751-767, Revised Effective Ionic Radii and Systematic Studies of Interatomic Distances in Halides and Chalcogenides. 4 Allred, A. L., Journal of Inorganic and Nuclear Chemistry, 17, 3-4 (1961) 215-221, Electronegativity Values from Thermochemical Data. 5 Huheey, J. E., Keiter, E. A., Keiter, R. L., 1993, Inorganic Chemistry – Principles of Structure and Reactivity, 4 th edition 6 Verma, A.S., Jindal, V.K., Journal of Alloys and Compounds, 485, 1-2 (2009) 514-518, Lattice Constant of Cubic Perovskites. 7 Moreira, R.L., Dias, A., Journal of Physics and Chemistry of Solids, 68, 8 (2007) 1617-1622, Comment on “Prediction of Lattice Constant in Cubic Perovskite”. 8 Jiang, L.Q., Guo, J.K., Liu, H.B., Zhu, M., Zhou, X., Wu, P., Li, C.H., Journal of Physics and Chemistry of Solids, 67, 7 (2006) 1531-1536, Prediction of Lattice Constant in Cubic Perovskite. 9 Wang, H., Wang, B., Li, Q., Zhu, Z., Wang, R., Woo, C.H., Physical Review B, 75, 24 (2007) 245209, First-principles Study of the Cubic Perovskites BiMO 3 (M = Al, Ga, In and Sc) 10 Geguzina, G.A., Sakhnenko V.P., Crystallography Reports, 49 (2004) 15-19, Correlation Between the Lattice Parameters of Crystals with Perovskite Structure. 11 Albina, J.M., Mrovec, M., Meyer, B., Elsasser, C., Physical Review B, 76, 16 (2007) 165103, Structure, Stability and Electronic Properties of SrTiO 3 /LaAlO 3 and SrTiO 3 /SrRuO 3 Electronegativity ; Perovskite (structure) ; Crystal structure ; Valence (chemistry) ; Oxide ; Lattice constant ; Ionic bonding ; Solid solution Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Inorganic Chemistry and Materials