The rise of artificial intelligence (AI), particularly machine learning (ML), is fundamentally reshaping how we pursue green chemistry and sustainable chemical processes. In this work, we explore the far-reaching impact of AI, leveraging techniques such as Artificial Neural Networks (ANNs), Random Forests (RF), and Bayesian Optimization (BO) across green chemistry, from catalysis and CO₂ capture to sustainable materials, drug development, and waste control. Indeed, established applications already abound, seen in catalyst design, solvent optimization for CO₂ capture, polymer innovation, pharmaceutical discovery, and waste management. While AI-driven approaches undeniably accelerate the discovery of new materials and processes, they also confront pressing challenges related to energy efficiency, scalability, and broader environmental impact. We therefore pinpoint critical knowledge gaps and provide actionable guidance for implementing AI models, emphasizing robust data management and rigorous model validation. Future considerations must also extend to the nascent potential of quantum computing, the critical ethical landscape, and the transformative implications of automated laboratories. Crucially, this study moves beyond a mere cataloging of applications; instead, the discussion is structured around deployment-critical questions, including method selection, reproducibility, interpretability, and the experimental meaning of performance metrics within the green chemistry context. It is argued in this study that the true value of AI lies not in a mere collection of tools, but as foundational infrastructure. Its scientific utility ultimately depends on robust data governance, cross-laboratory validation, and a clear alignment with core green chemistry objectives. By distilling current advancements and illuminating key challenges, we hope to better equip researchers and practitioners alike to harness AI’s transformative potential in securing a more sustainable future for chemical science.
Elkhatat et al. (Mon,) studied this question.