Client
This study was conducted by a joint team from Ghent University and Sunthetics. The goal: optimize a Suzuki–Miyaura cross-coupling reaction using real-world, industrial-grade datasets—where categorical variables like solvent and catalyst dominate the design space.
Challenge
- Identify the best combination of catalyst, solvent, and base for a complex cross-coupling reaction.
- Achieve this using minimal experimentation.
- Tackle the high-dimensional complexity of categorical variables, which are often not easily optimized by standard ML or DoE tools.
- Avoid the 768 experiments typically required by High-Throughput Experimentation (HTE) to explore the full design space.
Goal
- Find the global optimum for reaction yield.
- Cut down resource-intensive experimentation.
- Prove that small-data ML can rival HTE in speed and precision—even for categorical variables.
Approach & Solution
- SuntheticsML was deployed using proprietary:
- Supervised Learning (SL) and
- Active Learning (AL) algorithms
- No variable encoding or parameterization was required—SuntheticsML directly optimized categorical inputs like catalyst identity.
- Each optimization was initiated with 36 randomly selected experiments.
- The platform iteratively recommended 6 experiments per cycle: 5 likely to yield maximum performance + 1 random for exploration.
- Five independent "seeds" were used to evaluate repeatability and robustness.
Results & Metrics
- HTE benchmark:
- 768 experiments required to explore full combinatorial space and find optimal conditions.
- SuntheticsML outcomes:
- Average performance:
- 6 iterations, 72 experiments, 91% experiment reduction
- Best-case scenario:
- 2 iterations, 48 experiments, 94% reduction
- Worst-case scenario:
- 9 iterations, 84 experiments, 89% reduction
- Insight generated:
- Variable importance analysis showed the base had the strongest effect on yield—surprising the research team and shifting future optimization focus away from catalysts and solvents
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The Sunthetics Edge
“SuntheticsML matched the output of a fully combinatorial HTE campaign in a fraction of the time and cost—while revealing insights HTE didn’t, like the true variable driving reaction performance.”
Key Takeaways
The Sunthetics Edge
“SuntheticsML matched the output of a fully combinatorial HTE campaign in a fraction of the time and cost—while revealing insights HTE didn’t, like the true variable driving reaction performance.”
- 94% fewer experiments, same optimal results—machine learning outperformed expensive automation.
- Categorical ML breakthrough: SuntheticsML optimizes directly from labels without complex parameterization or one-hot encoding.
- New scientific insight: Base identity—not catalyst—was the key variable, guiding smarter future experimentation.
- Lower barrier to entry: SuntheticsML achieves elite-level results without the infrastructure or cost of high-throughput robotics.
- Repeatable & reliable: All five ML seeds converged to the correct optimum, confirming robustness.
