TITLE:
Computational Machine Learning-Based Prediction of Crystal Structure in Mixed B-Site Perovskite Oxide: LaFe1/3Co1/3Mn1/3O3
AUTHORS:
Lomus Hona, Carl White, Ram Krishna Hona
KEYWORDS:
Perovskite Oxide, Machine Learning, Crystal Structure Prediction, Mixed B-Site, LaFe1/3Co1/3Mn1/3O3, Tolerance Factor, Solid Oxide Fuel Cell, Electrocatalysis
JOURNAL NAME:
Journal of Minerals and Materials Characterization and Engineering,
Vol.14 No.4,
July
17,
2026
ABSTRACT: Mixed B-site perovskite oxides (LaBO3) 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 LaFe1/3Co1/3Mn1/3O3 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 LaFe1/3Co1/3Mn1/3O3 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 (σ2 = 0.00222 Å2) as the two dominant descriptors governing symmetry selection. The relatively high σ2 reflects the large Co3+ (LS)-Fe3+ /Mn3+ size mismatch (0.545 vs. 0.645 Å), and combined with the Jahn-Teller activity of Mn3+, drives cooperative GdFeO3-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.