Green Chemistry and Artificial Intelligence: A Synergistic Approach for Sustainable Synthesis
1 Usha Jaiswal,2 Dr. Ambuj Pandey, 3Anupama Pradhan,4 Suresh Kumar Bharty
1 Department of Chemistry,Chauksey Engineering College, Bilaspur, Chhattisgarh, India
2 Department of Chemistry Govt. Bilasa Girls P.G. (Auto.) College, Bilaspur, Chhattisgarh, India
3 Department of Chemistry Thakur Shobha Singh Govt. College Pathalgaon, Chhattisgarh, India
4 Department of Chemistry Dr. Jwala Prasad Mishra Govt. Science College, Mungeli, Chhattisgarh, India
3pradhananupamatss@gmail.com
Abstract: The integration of Green Chemistry and Artificial Intelligence (AI) presents a transformative path toward sustainable chemical synthesis. While green chemistry emphasizes minimizing toxicity, waste, and environmental harm through safer and more efficient processes, AI provides data-driven capabilities such as predictive modeling, retrosynthetic planning, process optimization, and automation. This synergy enables intelligent systems that predict reaction outcomes, optimize solvent and catalyst selection, minimize hazardous by-products, and align closely with the Twelve Principles of Green Chemistry. Leveraging technologies like machine learning, deep neural networks, cheminformatics, and generative models, AI speeds the creation of eco-friendly chemicals and low-energy reaction pathways while decreasing the need for significant lab research. Tools that facilitate solvent-free synthesis, atom economy, and high-throughput testing, such Molecular Transformer, Chemistry42, AlphaFold, and Graph Neural Networks (GNNs), are prime examples of this progression. The expanding importance of robotic systems and autonomous labs that employ AI to dynamically optimize synthesis methods in real time is also examined in the article. AI's concrete contributions to safer, more economical, and environmentally friendly chemical innovation are demonstrated by case studies in the pharmaceutical, agrochemical, and fine chemicals industries. Based on a comprehensive literature analysis and expert discussions from the ASLLA Symposium, the report highlights the value of multidisciplinary education, interpretable AI models, and carefully selected green datasets. Even if there are still issues with model generalisation, ethical implementation, and data governance, integrating AI literacy into chemistry courses and encouraging cooperation between academia, industry, and politics will be crucial to expanding this integration. In the end, this multidisciplinary approach promotes global objectives for responsible manufacturing, sustainable innovation, and climate action in addition to chemical intelligence.
Keywords: Green Chemistry, Artificial Intelligence, Cheminformatics,, Environmental Impact, Smart Chemical Design.
