EMERGING STRATEGIES IN PHASE-TUNED PEROVSKITE MATERIALS FOR EFFICIENT HYDROGEN EVOLUTION: A MACHINE LEARNING PERSPECTIVE

Phase-tuned perovskite materials have emerged as promising candidates for hydrogen production due to their excellent optoelectronic properties, flexible compositions, and structures.Phase engineering strategies, including strain induction, doping, and temperature-driven phase transformations, has significantly enhanced the tuning of bandgaps, charge transport, and catalytic activity.This review synthesizes current strategies for enhancing hydrogen evolution reactions (HER) efficiency in both oxide and halide perovskite systems through phase manipulation.The integration of machine learning (ML) into research accelerates the discovery of efficient HER catalysts by predicting optimal phase configurations, identifying novel compositions, and streamlining experimental efforts.Specifically, ML enhances phase tuning by analyzing vast compositional spaces, uncovering structure-property relationships, and guiding the design of phase-engineered perovskites with improved hydrogen evolution efficiency.Particular attention is directed towards AI-assisted studies on materials such as LaSrFeO and BaTiO, which demonstrate strong correlations between phase configuration and HER performance.This review uniquely bridges the gap between phase engineering and data-driven approaches, highlighting how ML augments rational design by reducing trial-and-error experimentation.This work also outlines key challenges, including phase stability, model interpretability, and data limitations while proposing future directions for the development of robust, scalable perovskite-based photocatalysts.The insights presented serve as a foundation for advancing clean and efficient hydrogen production technologies.