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

Machine Learning-Accelerated Density Functional Theory for Modeling the Physicochemical Properties of Oxide Perovskites as Oxygen Electrodes for Solid Oxide Electrochemical Cells The development of novel materials plays an important role in tackling global energy issues, such as efficient energy conversion, storage, and the advancement of sustainable technologies as solid oxide electrochemical cells. Perovskite oxides, characterized by the general formula A x A’ 1-x B y B’ 1-y O 6 , represent a highly adaptable group of materials. They are renowned for their structural flexibility, tunable electronic and ionic conductivity, and chemical stability [1] . These attributes position them as promising candidates for use in various technologies, including electrolyzer cells, fuel cells, and secondary batteries in which they serve primarily as oxygen electrodes. More recently, high-entropy perovskite oxides (HEPOs) have demonstrated enhanced thermal stability and lower operating temperatures. Moreover, due to their flexible composition, this class of perovskites allows for the analysis and substitution of critical raw materials. Nonetheless, identifying and refining materials with targeted electrochemical properties remains a significant challenge, largely due to the extensive compositional and structural variations that have yet to be thoroughly investigated [2] . Theoretical research on new oxide perovskites relies heavily on high-performance computational approaches, particularly first-principles methods. Density functional theory (DFT) is widely used to predict essential electrochemical properties, providing valuable insights into material behavior. Techniques like high-throughput DFT screening [3] enable the exploration of large material databases, identifying promising candidates based on specific criteria. Although highly precise, ab initio methods often require significant computational resources. Complementary to these methods, machine learning models trained on computationally-generated datasets are increasingly employed to predict material properties rapidly and efficiently, reducing the need for exhaustive simulations. These theoretical approaches not only accelerate the discovery process but also offer valuable insights into structure-property relationships, facilitating the rational design of perovskites [4] . This communication presents a database of key electrochemical properties, such as band gaps and oxygen vacancy formation energies, along with structural characteristics of perovskite oxides. This database is constructed by performing DFT+U calculations [5] to ensure accuracy for properties influenced by strong electronic correlations in simple perovskite systems, combined with public data [6] to create a more comprehensive database. Regarding machine learning algorithms, we employ two classes of algorithms: tree-based methods and artificial neural networks. Decision tree, random forest and gradient-boosted decision tree algorithms are selected for their robustness in modeling non-linear relationships and their excellent computational performance with tabular datasets. While tree-based algorithms are commonly used for tabular data, neural networks are employed to capture more complex patterns. Specifically, we utilize feedforward and convolutional neural networks to better identify and interpret intricate relationships [7] . Model performance is evaluated via root mean squared error (RMSE) and accuracy because they provide insights into both the magnitude of prediction errors and the overall correctness of the model. RMSE measures the square root of the average squared differences between predicted and actual values, highlighting larger errors. Accuracy assesses the model’s performance by comparing the number of correctly predicted instances to the total number of predictions. Additionally, the confusion matrix is used to assess classification performance by displaying the counts of true positives, false positives, true negatives, and false negatives, offering a detailed view of model accuracy and potential misclassifications. By combining these neural network architectures with tree-based models, we aim to evaluate the predictive capabilities of different machine learning techniques for estimating the electrochemical properties of perovskite materials. This approach seeks to accelerate the identification of promising HEPO candidates tailored for electrolytes and electrodes in solid oxide fuel cells applications, reducing computational costs. These predictions will be then validated through DFT+U calculations, followed by experimental synthesis and characterization. While the initial steps focus on a limited set of physicochemical characteristics, future works will expand to include a broader range of properties including both bulk and surface characteristics. Acknowledgments Dr. Barp 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] J. 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Density functional theory ; Oxide ; Perovskite (structure) ; Electrochemistry ; Ionic bonding ; Chemical stability ; Electrochemical energy conversion ; Structural stability ; Electrolysis Machine Learning in Materials Science ; Advancements in Solid Oxide Fuel Cells ; Perovskite Materials and Applications