How SuntheticsML Outperformed High-Throughput Experimentation in Cross-Coupling Chemistry

This collaboration between Ghent University and Sunthetics focused on optimizing categorical variables in a complex Suzuki-Miyaura cross-coupling. The joint team aimed to compare the predictive power and resource efficiency of SuntheticsML with traditional high-throughput experimentation (HTE)​.

• Determine the best-performing catalyst, base, and solvent combination from a large pool of categorical options.
  
  
• Handle high dimensionality in experimental design without requiring parameterization.
  
  
• Reduce the resource cost and scale of traditional HTE workflows, which demand hundreds of reactions and significant infrastructure.
  
  

• Achieve or surpass the performance of HTE using significantly fewer experiments.
  
  
• Demonstrate that ML can efficiently optimize categorical variables without prior assumptions or encoding.
  
  
• Reveal non-intuitive variables of influence to inform future R&D strategy.
  
  

• Used SuntheticsML’s proprietary Supervised + Active Learning (SL + AL) framework.
  
  
• No one-hot encoding or parameter mapping was needed—true categorical optimization was performed directly.
  
  
• Each ML seed (5 total) started from 36 randomly selected experiments (out of 768).
  
  
• At each iteration, SuntheticsML suggested 6 experiments:
  
  
  • 5 predicted optimal points
    
    
  • 1 exploratory point for model refinement
    
    
• The experiment recommendations adapted dynamically as performance data improved.
  
  

• Traditional HTE:
  
  
  • Required 768 experiments to map the full design space.
    
    
• SuntheticsML outcomes:
  
  
  • Average performance:
    
    
    • 6 iterations, 72 experiments, 91% experiment reduction
      
      
  • Best performance:
    
    
    • 2 iterations, 48 experiments, 94% experiment reduction
      
      
  • Worst case:
    
    
    • 9 iterations, 84 experiments, 89% reduction
      
      
• Speed to optimum:
  
  
  • Reached the same global maximum as HTE with a fraction of the cost and time.
    
    
• Insight from variable importance analysis:
  
  
  • Contrary to expectation, base selection had the largest influence on reaction yield.
    
    
  • Solvent and catalyst changes had significantly less predictive weight, shifting future optimization priorities.
    
    

"This study shows why categoricals don't have to be the bottleneck. With SuntheticsML, turned a 768-experiment challenge into a 48-experiment solution—without sacrificing accuracy. The model revealed that base selection—not catalyst or solvent—was the key driver of performance. That's the kind of insight you don't get from brute force."