Revolutionizing Green Chemistry through Artificial Intelligence and its Applications

Abstract

The integration of Artificial Intelligence (AI) into green chemistry has emerged as a transformative approach for developing sustainable and environmentally benign chemical processes. AI techniques such as machine learning, data analytics, and predictive modeling enable researchers to design eco-friendly synthesis pathways, optimize reaction conditions, reduce waste generation, and minimize energy consumption. By analyzing large chemical datasets, AI can identify greener solvents, catalysts, and reaction mechanisms that enhance efficiency while lowering environmental impact. Furthermore, AI-driven tools accelerate the discovery of sustainable materials and improve lifecycle assessment, supporting the principles of green chemistry. The application of AI also facilitates real-time monitoring and intelligent process control in industrial chemistry, leading to safer and more resource-efficient production systems. Despite challenges such as data availability, model transparency, and computational limitations, the synergy between AI and green chemistry holds significant potential for advancing sustainable innovation.