Maximizing Reaction Yield Through Bayesian Optimization
The Problem
Traditional Design of Experiments (DoE) methodologies become increasingly impractical as the number of experimental factors and their levels grow, leading to an exponential increase in required experiments. This results in excessive resource consumption—time, materials, and labor—while also complicating data analysis due to large datasets and potential confounding effects.
In optimizing reaction yield for a specific reaction step, constraints such as solvent and antisolvent selection, lab equipment setup, and operational feasibility further limit the feasibility of exhaustive experimentation. The reaction was influenced by five key factors—solvent (2 levels), reagent (5 levels), reagent addition flow rate (5 levels), agitation rate (4 levels), and temperature (6 levels).
A full factorial design would necessitate 1,200 experiments, rendering traditional approaches impractical within available time and resource constraints.
The Breakthrough
To address this challenge, Bayesian Optimization was implemented as a key strategy.
This approach intelligently prioritizes experimental conditions most likely to yield optimal results, drastically reducing the number of required experiments while accelerating decision-making.
The primary response variable—reaction yield—was used to measure success, reflecting the percentage of the desired product formed under specific conditions. Unlike traditional DoE methods, Bayesian Optimization follows a "learn as we go" approach, dynamically adjusting the experimental plan based on real-time data. This reduces unnecessary experiments and delivers faster, data-driven insights.
To start the process, an initial set of experiments was designed using Latin Hypercube Sampling, ensuring broad coverage of the design space. This provided a strong foundation for Bayesian Optimization to refine conditions efficiently.
The Experiment
Initial Screening. An initial screening was conducted to feed the Bayesian Optimization model predictions. This first screening was guided by the Latin Hypercube Sampling method to better cover the design space to be explored.
Bayesian Optimization Implementation. Gaussian Process Regression was applied together with machine learning models to predict the hyperparameters to be used in the predictions. This model provided the next experimental points to be explored in the lab and would serve for further improvement of the model.
The framework balanced exploration (identifying areas of high uncertainty) and exploitation (focusing on conditions likely to yield the best results).
Data Analysis. Experimental data was used to build response surface models for the reaction yield, allowing for visualization of the relationships between factors.
Interactions between factors (e.g., the interaction between temperature and flowrate) were identified to better understand the underlying reaction mechanisms.
Validation. The optimized conditions were validated by conducting experiments in the laboratory, performing further of the model if further improvement is needed.
The Impact:
Maximized Reaction Yield & Experimental Efficiency
Significant Experiment Reduction: Bayesian Optimization reduced the required experiments from 1,200 to a manageable subset, cutting both time and resource costs.
Optimized Reaction Conditions: The method successfully identified the optimal ranges for temperature, flow rate, solvent, reagent, and agitation rate, maximizing reaction yield efficiently.
Accelerated Decision-Making: The adaptive, data-driven approach enabled faster insights, making the entire experimental process more streamlined and effective.
By integrating Bayesian Optimization, experts achieved a faster, more efficient, and resource-conscious approach to experimentation, enabling smarter decision-making in process development.