A workshop given by Sterling Baird on August 22, 2023 - Accelerate Conference @ University of Toronto (https://www.accelerate23.ca/)
Virtually every real-world chemistry and materials informatics task involves optimizing multiple properties of interest, weighing trade-offs of between experiment value and cost, and optimizing many tunable parameters at once. While traditional design of experiments is often used, Bayesian optimization is a dramatically more efficient alternative in many cases. In this workshop, participants will learn about why we need Bayesian Optimization and about topics such as multi-objective, multi-fidelity, and high-dimensional Bayesian optimization.
Tutorial #1: https://colab.research.google.com/gist/sgbaird/e5a9b333154feabcb68a52dde6cbb9b0/ax-service-api.ipynb
Tutorial #2: Adapt the first tutorial and apply it to https://colab.research.google.com/github/sparks-baird/self-driving-lab-demo/blob/main/notebooks/ac-2023/bayes-opt/1.0-sgb-clslab-light-simple.ipynb