Title : Challenges and trends of green chemistry in the era of artificial intelligence
Abstract:
As global environmental concerns intensify, green chemistry has gained significant attention. Green chemistry, know as sustainable chemistry, is an innovative approach to chemical research and manufacturing that seeks to reduce harmful substances, designing safer chemicals, developing alternative technologies, efficient utilization of resources, minimizing waste, and improving energy efficiency. Green chemistry involves multiple subject areas, including chemistry, biology, materials science, engineering, etc. Green chemistry is key to solving big environmental problems. In the pharmaceutical industry, the synthesis of drugs through green chemical methods can avoid the use of organic solvents with carcinogenic properties, thus reducing the risks of drug production to the environment and human health.Green chemistry minimizes or even eliminates the formation of hazardous substances by selecting safer and more environmentally friendly raw materials and reaction paths.Green chemistry contributes to the sustainable development of the ecological environment and reduces environmental pollution caused by industrialization, such as ozone layer destruction, atmospheric pollution, Marine pollution, etc.This abstract explores the emerging trends, opportunities, and challenges at the intersection of green chemistry and AI, highlighting key advancements and directions.
In recent decades the integration of artificial intelligence (AI) into green chemistry represents a transformative shift in sustainable scientific research and chemical industrial applications.AI-driven technologies, including machine learning (ML), deep learning, and predictive modeling, are revolutionizing chemical research by accelerating discovery, optimizing processes, and minimizing environmental impact.There is enormous potential for synergies between green chemistry and AI. AI is helping researchers and industries design more sustainable and efficient chemical systems. Artificial Intelligence (AI) can analyze vast amounts of data to identify promising new materials for green chemistry applications. ML algorithms can predict the properties and performance of potential materials, reducing the time required for experimentation and development. Despite these advancements, several challenges persist,including Data quality and availability,Dependence on Fossil Fuels and Traditional Chemical Processes, technological limitations, Economic and Industrial Barriers,Green Catalysis and Alternative Reaction Pathways,Green Solvents, Regulatory and Policy Challenges,etc.
One of the most significant trends of Green chemistry is the application of AI in designing eco-friendly chemical processes.Data quality and availability remain critical barriers, as AI models require large, high-quality datasets to generate reliable predictions.Another key trend is that green chemistry also needs to strengthen ethical review and regulation to ensure that the application of technology is in line with ethical norms and social responsibility. Through measures such as data privacy protection, algorithmic fairness, ethical responsibility transparency, social impact fairness, and interdisciplinary cooperation, it can be effectively addressed to ensure the sustainable development of AI technology in green chemistry.Green chemistry is evolving rapidly, driven by biotechnology, artificial intelligence, and materials science.Looking ahead, the convergence of AI and green chemistry holds immense potential but demands a balanced approach. Future research should focus on developing explainable AI models,Green Catalysis and Enzyme Engineering, Sustainable Polymers and Biodegradable Materials, Green Energy and Sustainable Chemical Processes, Application of AI ,Ethical review and regulation,and Interdisciplinary cooperation and knowledge sharing .
