Computational Machine Learning-Based Prediction of Crystal Structure in Mixed B-Site Perovskite Oxide: LaFe 1/3 Co 1/3 Mn 1/3 O 3

Abstract

Mixed B-site perovskite oxides (LaBO 3 ) are critical materials for energy applications including solid oxide fuel cells (SOFCs), oxygen evolution reaction (OER) catalysts, and electrocatalytic hydrogen production. Predicting crystal symmetry in complex compositions such as LaFe 1/3 Co 1/3 Mn 1/3 O 3 remains challenging due to the competing effects of ionic size mismatch, electronegativity differences, and Jahn-Teller activity among co-occupying B-site cations. Here we apply three supervised machine learning (ML) classifiers—Random Forest (RF), Gradient Boosting (GB), and Support Vector Machine (SVM)—trained on a curated dataset of published La-based perovskite structures, to predict the crystal symmetry of this ternary B-site composition before experimental synthesis. Experimental validation confirms orthorhombic symmetry ( Pnma , a = 5.510 Å, b = 7.810 Å, c = 5.528 Å) for LaFe 1/3 Co 1/3 Mn 1/3 O 3 synthesized by solid-state reaction at 1250˚C, providing independent ground-truth validation. All three ML models unanimously predicted orthorhombic symmetry with probabilities ranging from 0.824 to 1.000. The models were trained using seven physically meaningful descriptors: Goldschmidt tolerance factor, octahedral factor, B-site ionic radius variance, electronegativity variance, average B-site radius, formal charge variance, and Jahn-Teller activity. Feature importance analysis identifies the tolerance factor (t = 0.970) and B-site ionic radius variance (σ 2 = 0.00222 Å 2 ) as the two dominant descriptors governing symmetry selection. The relatively high σ 2 reflects the large Co 3+ (LS)-Fe 3+ /Mn 3+ size mismatch (0.545 vs. 0.645 Å), and combined with the Jahn-Teller activity of Mn 3+ , drives cooperative GdFeO 3 -type octahedral tilting that stabilizes the orthorhombic Pnma structure. Cross-validation accuracies range from 0.963 to 1.000 across models. This work demonstrates that descriptor-based ML can reliably guide experimental synthesis by pre-screening orthorhombic perovskites, substantially reducing trial-and-error effort and providing an efficient computational platform for energy-related oxide research at UTTC.