This Might Be the Future of DoE
Understanding Bayesian Optimization: A Smarter Way to Optimize Experiments
Science is about solving problems through experiments. In chemistry, this might mean maximizing the yield of a reaction or improving the hardness of a coating. Traditional methods like full factorial or fractional design have been around for over a century. While these approaches are reliable, they can be resource-intensive. It’s time to explore something newer and more advanced: Bayesian Optimization (BO).
What is Bayesian Optimization?
Bayesian Optimization is like the “smart cousin” of traditional Design of Experiments (DoE). Instead of planning and running all your experiments upfront, BO starts small. You begin with a handful of initial tests, train a machine learning model, and use it to decide the next most promising experiments to meet your goal. This iterative process saves you both time and resources.
Think of it like searching for information in a book. You could read the whole book from start to finish, but that takes forever. Instead, you flip through the most promising chapters first, zeroing in on the sections that are most likely to have the answers you need. Bayesian Optimization works similarly—it narrows down the possibilities and guides you to the answer you’re looking for more quickly.
How Does Bayesian Optimization Work?
Bayesian Optimization is an iterative process with five main steps:
Start Small: Begin with a small number of experiments, typically spread across the design space. These can be chosen randomly or through more strategic methods like Latin hypercube design or fractional factorial design. The goal is to get diverse starting data.
Make Predictions: Use a machine learning model (often a Gaussian process) to predict outcomes and estimate uncertainty across the entire design space. The model essentially maps out where your best results might be hiding.
Pick the Next Experiment: The system balances two competing goals:
Exploitation: Focus on areas that already look promising.
Exploration: Test areas where the model has less confidence.
This balance ensures that you don’t miss hidden opportunities while improving the known good areas.
- Run the Experiment: Perform the selected experiments, record the results, and feed this new data back into the model. Each iteration refines the model’s understanding of the design space.
- Repeat: Keep iterating until you find the best solution—or until you run out of time or resources.
Example: Bayesian Optimization in Action
Let’s say you’re trying to optimize the yield of a chemical reaction by adjusting the temperature. This is a simple example, but it helps explain how Bayesian Optimization works.
Start by running a few experiments—for example, we choose 4 experimental runs, evenly spread across the design space. The data from these tests is then used to train a machine learning model that predicts outcomes and uncertainties across the entire design space.
Based on these initial runs, the model selects the next experiments to perform. With each iteration, you can see the model’s confidence grow as it refines its predictions. The selection balances two goals: exploitation—focusing on areas likely to give high yields—and exploration—testing areas where the model’s predictions are less certain.
At this stage, the model has likely identified the most promising conditions for maximum yield. In reality, we wouldn’t know this with complete certainty—we might only have an educated guess based on our expert knowledge. To confirm, we could run a final set of experiments to verify the results. If nothing changes, we’d stop here and conclude the experiment.
More to come
This was just a quick introduction to Bayesian Optimization and how it can transform experimental design. It’s a powerful tool, especially for tackling complex problems more quickly and efficiently - at least in theory. I’ve only recently started exploring Bayesian Optimization myself. I plan to dive deeper into real-life applications and will share updates on how it goes.