A mini review on the applications of artificial intelligence (AI) in surface chemistry and catalysis

Abstract

This review critically analyzes the incorporation of artificial intelligence (AI) in surface chemistry and catalysis to emphasize the revolutionary impact of AI techniques in this field. The current review examines various studies that using AI techniques, including machine learning (ML), deep learning (DL), and neural networks (NNs), in surface chemistry and catalysis. It reviews the literature on the application of AI models in predicting adsorption behaviours, analyzing spectroscopic data, and improving catalyst screening processes. It combines both theoretical and empirical studies to provide a comprehensive synthesis of the findings. It demonstrates that AI applications have made remarkable progress in predicting the properties of nanostructured catalysts, discovering new materials for energy conversion, and developing efficient bimetallic catalysts for CO2 reduction. AI-based analyses, particularly using advanced NNs, have provided significant insights into the mechanisms and dynamics of catalytic reactions. It will be shown that AI plays a crucial role in surface chemistry and catalysis by significantly accelerating discovery and enhancing process optimization, resulting in enhanced efficiency and selectivity. This mini-review highlights the challenges of data quality, model interpretability, scalability, and ethical, and environmental concerns in AI-driven research. It highlights the importance of continued methodological advancements and responsible implementation of artificial intelligence in catalysis research.