AI-Assisted Discovery of Sustainable Chemical Processes: Reducing Energy Use and Environmental Impact

Gaurav Jha

AI-Assisted Discovery of Sustainable Chemical Processes: Reducing Energy Use and Environmental Impact

Author(s)	Gaurav Jha
Country	United States
Abstract	The transition toward sustainable chemical manufacturing requires new approaches for reducing energy consumption, material waste, greenhouse-gas emissions and environmental impacts while maintaining economic performance and product quality. Artificial intelligence (AI), machine learning, computational chemistry and automated experimentation are increasingly enabling researchers to accelerate the discovery and optimisation of chemical processes. This paper examines how AI-assisted approaches can support sustainable process discovery, focusing on reaction prediction, catalyst development, solvent selection, process optimisation, energy minimisation, waste reduction and life-cycle-informed decision-making. The study proposes an integrated computational framework connecting chemical knowledge, experimental data, machine-learning models, process simulations and automated laboratory systems. Particular attention is given to multi-objective optimisation, where chemical yield is considered alongside energy demand, carbon emissions, toxicity, solvent use and waste generation. The paper also discusses the limitations of current AI approaches, including data quality, sparse experimental datasets, model interpretability, chemical-space complexity, scale-up uncertainty and the difficulty of translating laboratory-level optimisation into industrial processes. A sustainable AI-assisted discovery framework is proposed in which algorithms identify promising reaction and process conditions, automated experiments generate new evidence, and iterative learning progressively improves both chemical performance and environmental outcomes. The analysis indicates that AI can become an important enabling technology for sustainable chemistry when environmental metrics are embedded directly into the design process rather than evaluated only after a chemical process has been developed. Future progress will depend on integrating AI with mechanistic chemistry, life-cycle assessment, process engineering and automated experimentation.
Keywords	Artificial Intelligence, Sustainable Chemistry, Green Chemistry, Chemical Process Discovery, Machine Learning, Process Optimisation, Catalysis, Energy Efficiency, Life-Cycle Assessment, Sustainable Manufacturing.
Field	Engineering
Published In	Volume 7, Issue 5, September-October 2025
Published On	2025-10-13

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American Journal of Advanced Multidisciplinary Research and Innovation

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

AI-Assisted Discovery of Sustainable Chemical Processes: Reducing Energy Use and Environmental Impact

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