Artificial Intelligence-Assisted Sustainable Reaction and Process Optimization

AI-assisted sustainable reaction and process optimization represents an important emerging interface between chemistry, data science, automation and green chemistry. Machine learning can help predict reaction outcomes, select catalysts and solvents, optimize reaction conditions, reduce experimental requirements, and support continuous-flow and automated chemical synthesis. Its greatest potential lies in multi-objective optimization, where chemical performance is evaluated simultaneously with waste generation, energy consumption, toxicity, cost and life-cycle environmental impact. Nevertheless, AI should not be considered a substitute for chemical understanding or sustainability assessment. The quality of training data, model interpretability, computational energy requirements, experimental reproducibility and appropriate definition of system boundaries remain important challenges. Ultimately, the combination of AI-driven prediction, automated experimentation, green chemistry principles and life-cycle assessment could enable a new generation of chemical processes that are not only more efficient but also safer, less wasteful and more compatible with environmental sustainability.

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