Hello Community,
As we look to accelerate the transition to sustainable feedstocks and safer chemical design, I’m interested in discussing the role of Artificial Intelligence (AI) and Machine Learning (ML) in the initial discovery phase.
While established tools like the PMI (Process Mass Intensity) Calculator and LCA (Life Cycle Assessment) software are invaluable for optimizing existing processes, it remains a challenge to accurately assess the environmental impact or "greenness" of a reaction during the early R&D or "paper chemistry" stage—where data is often sparse or non-existent.
I would love to hear from the innovators and researchers in this group:
Predictive Modeling: Are there specific AI models or open-access databases you recommend for predicting toxicity, persistence, or bioaccumulation before a molecule is even synthesized?
Workflow Integration: How can we better integrate these digital tools into the standard organic chemistry workflow so that Design for Degradation (Principle 10) is a priority from Day 1?
The Data Gap: How do we overcome the lack of "negative data" (failed, non-green reactions) in public databases to train more accurate sustainability models?
I believe that bridging the gap between data science and green synthesis is key to the next decade of innovation. I look forward to hearing your experiences or any case studies you can share!
