AI-Assisted Design and Optimization of Sustainable Organic Synthesis Pathways Using Green Catalytic Systems

Abstract

In recent years, the need for environmentally green chemical manufacturing has risen, stimulating growing interests in the development of environmentally friendly pathways of organic synthesis. This research aims at promoting Green Catalytic architectures for the design and optimization of sustainable processes for organic synthesis with the aid of Artificial Intelligence (AI). Using machine learning and deep learning models, the optimized reaction conditions were predicted, the synthesis efficiency was enhanced and the environmental impact was reduced. Several different green catalytic systems such as heterogeneous catalyst, nanocatalyst, biocatalyst and organocatalyst were comparatively evaluated by considering reaction yield, reaction selectivity, catalyst repeatability, and energy efficiency. The findings showed that optimization using artificial intelligence could dramatically enhance the performance of the reaction in terms of reaction time, consumption of the solvent, catalyst load and less waste produced, when compared to the typical synthesis routes. In the assessed catalytic systems, the nanocatalysts had the best catalytic activity and stability under optimum conditions. Sustainability assessment through green chemistry metrics indicated better atom economy, lower E-factor values and less carbon emissions in the synthetical pathways assisted with the aid of AI. The results indicate that artificial intelligence can enhance the chemical industry's sustainability, including the use of green catalytic processes, contributing to more resource efficient, cleaner, and safer production methods. Such strategy can be very useful to pursue sustainable industrial chemistry and future automatized synthesis systems.