With the emergence of new Bayesian optimization tools for the physical sciences, it is important to understand their strengths and weaknesses, reduce the barrier to use, and adapt them for real-world problems. In this virtual hackathon, we will put these tools to the test! Scientists from the Acceleration Consortium @ University of Toronto and Merck KGaA are hosting a 2-day virtual hackathon (March 27 & 28: 9am-1pm ET) for researchers to work collaboratively in teams on projects related to Bayesian optimization. Researchers can propose projects from a range of topics such as applying algorithms to existing benchmarks, developing new benchmark tasks, creating instructional tutorials, proposing real-world chemistry and materials optimization tasks, and more. After the hackathon, results will be collated and presented in a scholarly article. Join us to explore, collaborate, innovate, and contribute to the advancement of Bayesian optimization for the physical sciences!

👉 See https://ac-bo-hackathon.github.io/ for more info.

The website repository is hosted at https://github.com/AC-BO-Hackathon/ac-bo-hackathon.github.io.