Accelerating accurate multi-property prediction of perovskites via meta-heuristic algorithm tuned machine learning

The vast compositional space of ABX 3 perovskites presents a dual bottleneck for materials discovery: prohibitive screening costs and inefficient hyperparameter optimization (HPO) required to develop accurate predictive models. To address this computational challenge, we introduce a systematic machine learning framework that rigorously benchmarks six meta-heuristic algorithms (MHAs) for HPO, systematically quantifying the critical trade-off between prediction accuracy and computational cost. The optimized models achieve competitive predictive performance, with test-set R 2 values exceeding 0.9653 for formation energy, 0.9196 for energy above hull, and 0.8669 for bandgap, alongside a thermodynamic stability classification accuracy of 0.8646. Our analysis reveals that the Sooty Tern Optimization Algorithm (STOA) and Particle Swarm Optimization (PSO) show a favorable balance, confirming that the ideal MHA choice is task-specific—a key finding for efficient model development. Beyond prediction, the interpretable framework uses Shapley analysis to uncover core physicochemical drivers, revealing, for instance, that stability is strongly influenced by range electronegativity and space group, while band gaps are governed by formation energy and transition metal fraction. This accelerated, insight-driven framework culminated in the high-throughput screening of 3864 compounds, identifying 54 stable, lead-free candidates for photovoltaics (PV) and 51 for photoelectrochemical (PEC) applications.