AI-DRIVEN DESIGN, CHARACTERIZATION AND ANALYTICAL ASSESSMENT OF GREEN CATALYSTS FOR SUSTAINABLE CHEMISTRY

Abstract

The integration of artificial intelligence (AI) and machine learning (ML) with green chemistry principles has revolutionized catalyst design, characterization, and performance assessment for sustainable chemical processes. This comprehensive review examines recent advances in AI-driven approaches for developing environmentally benign catalysts, encompassing computational methods including density functional theory (DFT), neural networks, random forests, and graph neural networks (GNNs). We analyze 30 high-impact studies demonstrating how AI accelerates catalyst discovery through data-driven screening, property prediction, and inverse design strategies. Key findings reveal that ML models achieve prediction accuracies exceeding R² > 0.98 for catalytic performance metrics, while AI-optimized green catalysts demonstrate conversion efficiencies up to 97% with enhanced selectivity and reduced environmental footprints. Advanced characterization techniques coupled with AI, including operando X-ray absorption spectroscopy (XAS) and high-throughput screening, enable real-time monitoring of catalyst dynamics at atomic resolution. Sustainability assessments indicate that AI-designed catalysts reduce greenhouse gas emissions by up to 40%, minimize waste generation, and promote circular economy principles through renewable feedstock utilization. This article synthesizes methodologies, performance benchmarks, and green metrics, providing a roadmap for next-generation sustainable catalysis driven by intelligent computational frameworks.