Machine learning -driven predictions of lattice constants in ABX3 Perovskite Materials

In pursuing accelerated material design, predictive modeling of lattice constants in perovskite materials has become essential for semiconductors, optoelectronics, and thermoelectrics applications. This study introduces machine learning models—Support Vector Regression (SVR), Gaussian Process Regression (GPR), Artificial Neural Networks (ANN), and Ensemble Regression Trees (ERT) trained on a comprehensive dataset incorporating ionic radii, electronegativity, density, and atomic number of perovskite constituents. Each model was optimized via the Bayesian optimization method to enhance accuracy in predicting lattice parameters. Notably, the GPR model achieved the highest precision, with an R 2 of 100% in training and 99% in testing, underscoring its capacity to capture intricate structural correlations. Results indicate that machine learning, specifically GPR, can provide an efficient and scalable alternative to traditional experimental methods, positioning these models as invaluable tools for high-throughput screening in material discovery. This approach presents a promising pathway for advancing computational material science, enabling rapid and precise lattice constant predictions to facilitate innovations across diverse technological domains. • Machine learning models accurately predict lattice constants in perovskite materials. • The GPR model achieves the highest predictive precision with 100% R 2 in training and 99% in testing. • Bayesian optimization enhances model accuracy by refining critical hyperparameters. • The study provides a scalable, high-throughput solution for rapid material discovery and design.