Artificial Intelligence for Sustainable & Environmentally Benign Chemical Science

Abstract

Abstract Green chemistry seeks to prevent pollution at its source by reducing waste, energy demand, hazardous substances, and resource consumption. Artificial intelligence (AI), especially machine learning (ML), can strengthen this approach by learning from chemical and process data, predicting outcomes, screening large chemical spaces, and optimizing experiments. Recent literature identifies applications ranging from catalyst discovery and reaction optimization to CO₂ capture, sustainable polymers, waste management, water treatment, and process control. A 2026 review in Applied Intelligence highlights artificial neural networks, random forests, and Bayesian optimization as important approaches across these areas, while also emphasizing data quality, validation, energy use, and scalability as major concerns. (see References 1–6) This project reviews how AI can support the principles of green chemistry and proposes a practical research framework in which sustainability metrics are included directly in model objectives. The central idea is that an AI system should not merely maximize chemical yield; it should seek a balanced outcome involving yield, selectivity, waste, toxicity, energy, solvent impact, and resource efficiency. The report concludes that AI can become an enabling technology for greener chemistry when models are transparent, experimentally validated, and evaluated against environmental as well as technical performance.