Role of Artificial Intelligence in Heterogeneous Catalyst

Heterogeneous catalysis plays a vital role in present-day chemical industries and supports numerous large-scale operations related to energy production, petrochemical processing, and environmental remediation. Despite its industrial importance, the systematic design of efficient catalysts is still difficult because catalytic reactions are governed by several interconnected factors operating at different length and time scales, including electronic interactions, surface processes, reaction pathways, and mass and heat transport at the reactor level. When combined with first-principles simulations and experimental results, ML(Machine Learning) can lower computational costs and allow researchers to explore a wider range of catalysts. Heterogeneous catalysis, ML has been used for tasks like discovering new catalysts quickly, predicting adsorption energies, modeling reaction rates, evaluating uncertainties, and supporting automated experiments. Artificial intelligence (AI) is increasingly influencing heterogeneous catalysis by accelerating atomistic simulations, kinetic modeling, and experimental analysis. A persistent challenge is the connection between intrinsic reaction kinetics and experimentally accessible observables, due to the multiscale nature of catalytic systems and the indirect character of measurements. Recent progress in machine-learned force fields, micro kinetic modeling, reactor simulations, and operand techniques enables rapid exploration of large chemical and parameter spaces. Ambiguity persists as multiple mechanisms fit the same data, while AI-driven self-driving models enable rapid catalyst prediction and optimization.