Bayesian Optimization Hackathon for Chemistry and Materials

Publication date: 22 Giu 2025

PreprintSource: OPENALEXOpenAlex type: preprintOpen Access
Authors: Sterling G. Baird, Mehrad Ansari, Zartashia Afzal, Qianxiang Ai, Alexander Al‐Feghali, Mathieu Alain, Matias Altamirano, Thomas Andrews, Andy S. Anker, Rija Ansari, Samuel Ampofo Appiah, Raul Astudillo, Ruhana Azam, Mohammed Azzouzi, Suneel Kumar BVS, Ben Blaiszik, Anna S. Borisova, Andres M. Bran, Pengfei Cai, Ting-Yeh Chen, Curtis Chong, Samantha Corapi, Mark P. Croxall, Gbetondji Dovonon, José Manuel Nápoles-Duarte, Andrew Falkowski, Giuseppe Fisicaro, Martin Fitzner, Quinn Gallagher, Sabah Gaznaghi, Jérôme Genzling, Christoph Griehl, Ryan‐Rhys Griffiths, Taicheng Guo, Kehan Guo, Nipun Gupta, Ankur K. Gupta, Mohammad Haddadnia, Yuyang Han, Joscha Hoche, Alexander V. Hopp, Marko Huang, Ayodeji Ijishakin, Ramsey Issa, Yeonghun Kang, Jungtaek Kim, Akshay Kudva, Rubén Laplaza, Magdalena Lederbauer, Shi Xuan Leong, Paul W. Leu, V. Li, Mingxuan Li, Tao Liu, Stanley Lo, Jakub Lála, Osman Mamun, Owen A. Melville, Michail Mitsakis, Cameron S. Movassaghi, Madhav R. Muthyala, Marcel Müller, Bozhao Nan, Duc Nguyen, Daniele Ongari, Anthony Onwuli, Can Özkan, Sergio Pablo‐García, Elton Pan, Ratish Panda, Hyun Suk Park, Jaehee Park, Dieter Plessers, Tobias Plötz, Ella Miray Rajaonson, Bojana Ranković, Jarett Ren, Rim Rihana, Jurğis Ruža, Akhil S. Nair, Carter Salbego, Erick Lopez Saldivar, Arifin San, Christina Schenk, Stefan P. Schmid, Dylan Schubert, Philippe Schwaller, Cher Tian Ser, Maitreyee Sharma Priyadarshini, Yuxin Shen, Kevin Shen, Jiale Shi, Farshud Sorourifar, Adrian Šošić, Taylor D. Sparks, Jan C. Spies, Felix Strieth‐Kalthoff, Suraj Sudhakar, Aditya Sundar, Alessio Tamburro

The Acceleration Consortium and Merck KGaA hosted a 2-day virtual hackathon on March 27- 28, 2024, bringing together scientists to explore, collaborate, and innovate in the field of Bayesian optimization for the physical sciences. Participants were encouraged to select or develop Bayesian optimization algorithms, apply them to benchmarking tasks, design new benchmarks, create instructional tutorials, and describe real-world applications. With over 100 participants across 69 academic, industry, and government organizations located in 59 cities, 19 countries, and 4 continents, this was a global event. The outputs from this event, including developed algorithms, benchmarks, and tutorials, will serve as valuable resources for the research community, in addition to the new skills learned and connections formed. Released projects and general information are available at https://ac-bo-hackathon.github.io/ and other locations linked from individual project pages. This event demonstrates the potential of community-driven research efforts to accelerate advances in Bayesian optimization in chemistry and materials science.

Origin
ChemRxiv
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