• SUJITH S I

Green chemistry aims to minimize environmental impact by reducing waste, replacing hazardous substances, and improving energy efficiency. However, traditional research methods are often slow, costly, and reliant on trial-and-error experimentation. Artificial Intelligence (AI) and Machine Learning (ML) have greatly improved green chemistry by making it easier to predict outcomes, improve reaction processes, and speed up the search for environmentally friendly materials. This study explores AI and ML applications in key areas, including catalyst design, process optimization, solvent selection, and biodegradable material development. AI-driven catalyst discovery has led to significant advancements, such as IBM Research’s AI-designed hydrogen production catalyst (2023), which improved reaction efficiency by 20%, and Stanford University’s AI-assisted CO₂ capture technology (2022), reducing industrial carbon emissions. Additionally, the University of Edinburgh developed an AI model (2023) that predicts eco-friendly solvents with 87% accuracy, reducing hazardous waste by 30%. AI-powered process optimization has also benefited industries, with Pfizer and Merck using ML algorithms to optimize pharmaceutical manufacturing, cutting energy consumption by 25%, and the University of Toronto enhancing biofuel production efficiency by 35% using AI-driven adjustments. The application of AI in material discovery has facilitated the development of sustainable solutions, such as MIT’s AI-powered biodegradable plastics (2023), which decompose 50% faster than conventional plastics, and Google DeepMind’s collaboration with Nestlé and Unilever (2024) to design AI-optimized sustainable packaging materials. AI-driven advancements have also extended to battery technologies, with Tesla adopting AI-based materials discovery to develop cobalt-free battery alternatives, promoting sustainability in the energy sector. Although significant progress has been made, some challenges still exist, such as the scarcity of chemical reaction data, the difficulty in interpreting AI models, and the need for their smooth integration into conventional chemical processes. Regulatory and ethical concerns further necessitate standardized AI frameworks and open-access chemical databases to ensure transparency and compliance. Initiatives such as the European Union’s 2025 GreenTech Initiative aim to address these limitations by promoting AI-driven sustainable chemistry research. Looking ahead, AI’s role in green chemistry is expected to expand further with quantum computing, robotics, and nanotechnology integration, enabling fully automated, waste-free chemical processes. Through ongoing research, funding, and collaboration across various disciplines, AI and ML will significantly contribute to developing a more sustainable and eco-friendly chemical industry.

Artificial Intelligence, Machine Learning, Green Chemistry, Catalyst Design, Process Optimization, Sustainable Materials, Environmental Sustainability.

"Artificial Intelligence and Machine Learning in Green Chemistry: A Future Perspective", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 3, page no.h646-h655, March-2025, Available :http://www.jetir.org/papers/JETIR2503780.pdf

"Artificial Intelligence and Machine Learning in Green Chemistry: A Future Perspective", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 3, page no. pph646-h655, March-2025, Available at : http://www.jetir.org/papers/JETIR2503780.pdf