Client

A synthetic chemistry research group optimizing conditions for a Suzuki–Miyaura cross-coupling reaction—a staple in pharmaceutical and materials R&D. The team faced challenges identifying ideal solvent–base pairs from a vast design space.

Challenge

• Identify the optimal solvent–base combination to maximize product yield (Ar1–Ar2) using minimal experiments.
  
  
• Navigate the complexity of categorical variables (solvents and bases), which cannot be optimized using traditional numerical approaches.
  
  
• Avoid inefficient combinatorial screening that previously required 81 experiments to find a >75% yield.
  
  

Goal

• Reach or exceed a 75% yield.
  
  
• Achieve this with fewer than 15 experiments.
  
  
• Uncover non-obvious or previously overlooked solvent–base combinations.

Approach & Solution

• Used SuntheticsML’s Bayesian Optimization, powered by proprietary Supervised Learning (SL) and Active Learning (AL).
  
  
• Encoded categorical variables with real chemical meaning:
  
  
  • Solvents described by dielectric constant and polarity.
    
    
  • Bases described by pKa and ionization energy.
    
    
• ML algorithm generated predictions and selected the most promising combinations for testing, iterating every two experiments.
  
  

Results & Metrics

• Final best-performing combination:
  
  
  • EtOH + KOtBu → 81% yield
    
    
• Performance progression:
  
  
  • Started at 7% yield with Toluene + NaOtBu
    
    
  • Gradual increase across 4 model iterations, ending at 81%
    
    
• Total number of experiments:
  
  
  • Only 12, guided across 5 iterations
    
    
• Algorithm-recommended combinations:
  
  
  • Included candidates that researchers had not previously considered or had erroneously ruled out
    
    
• Corrected experimental error:
  
  
  • An earlier false negative (0% yield for EtOH–KOtBu) was overturned thanks to the model’s suggestion, revealing experimental mislabeling
    
    
• Compared to empirical (combinatorial) approach:
  
  
  • 81 experiments needed to achieve 83% yield (DME + KOH)
    
    
  • SuntheticsML achieved 81% in just 12 experiments
    
    
  • → 85% experiment reduction
    
    

The Sunthetics Edge

“SuntheticsML not only guided to an unexpected high-yield result but also flagged an experimental error that would've led them astray. It saved time, materials, and gave us confidence in our choices.”

Key Takeaways

• Categorical optimization is solvable: Proper parameterization unlocks categorical ML use cases.
  
  
• ML sees what humans miss: EtOH–KOtBu was dismissed by researchers but identified as optimal by the model.
  
  
• Fewer iterations, better outcomes: Just 5 modeling iterations were needed to reach 81% yield.
  
  
• Data-light, insight-rich: ML-based parameter tuning beats trial-and-error even with a small dataset.
  
  
• Major reduction in experimentation: 85% fewer tests, less waste, and faster progress.
  

‍