Machine Learning-Assisted Design and Optimization of Nanocatalysts for Green Hydrogen Production in Sustainable Energy Systems

Green hydrogen, produced through renewable electricity-driven water electrolysis, represents one of the most consequential materials challenges of the twenty-first century energy transition. The core bottleneck is catalytic: no earth-abundant material simultaneously satisfies the thermodynamic, kinetic, stability, and economic requirements imposed by gigawatt-scale deployment of hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) catalysis. The chemical space of candidate nanocatalysts spanning transition metal alloys, phosphides, sulfides, carbides, single-atom catalysts, transition metal dichalcogenides, and two-dimensional MXenes - is combinatorially intractable by conventional experimental or density functional theory (DFT) screening alone. Machine learning (ML) has emerged as a structurally necessary complement to first-principles theory: it enables surrogate modeling of adsorption energetics across millions of candidate surfaces, active learning-guided exploration of high-dimensional compositional spaces, and generative inverse design of structures conditioned on target catalytic properties. This review synthesizes the current state of ML-assisted nanocatalyst discovery across these material families. Particular emphasis is placed on graph neural network architectures, Bayesian optimization frameworks, foundation models and universal neural network potentials, multi-objective design under experimental uncertainty, and the expanding roles of MXenes and single-atom catalysts as model systems for descriptor-based design. The integration of computational electrochemistry methods including explicit solvation, grand canonical DFT, transfer learning, and multi-fidelity learning with experimental validation platforms is examined critically. Persistent limitations, including out-of-distribution generalization failure, data quality constraints, the reproducibility crisis in nanocatalysis, and the gap between vacuum DFT predictions and operando catalytic behavior, are addressed with analytical rigor. Emerging developments in foundation models, autonomous self-driving laboratories, multi-agent AI systems, and closed-loop experimentation are assessed as transformative near-term directions. The central argument of this review is that ML does not merely accelerate catalyst discovery - it constitutes a new epistemological framework for interrogating structure-activity relationships in complex heterogeneous systems, with implications that extend well beyond the hydrogen production domain.

کلیدواژه ها:

green hydrogen ، machine learning ، nanocatalyst design ، graph neural networks ، MXenes