# Revolutionizing Solvent-Base Selection with Parametrized Optimization

81% Yield Achieved in 85% Fewer Experiments

#### Quick summary

Learn how Sunthetics maintains security and compliance standards for its AI driven experiment optimization platform.

##### Industry

Pharmaceutical and Materials R&D

#### **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.