Anubis: Bayesian optimization with unknown feasibility constraints for scientific experimentation
Model-based optimization strategies, such as Bayesian optimization (BO), have been deployed across the natural sciences in design and discovery campaigns due to their sample efficiency and flexibility.
Authors
- Riley J. Hickman (ORCID: https://orcid.org/0000-0002-5762-1006)
- Yingbing Zou (ORCID: https://orcid.org/0009-0001-5944-2829)
- Matteo Aldeghi (ORCID: https://orcid.org/0000-0003-0019-8806)
- Alán Aspuru-Guzik
- Gary Tom
Institutions
- University of Toronto (CA)
- Vector Institute (CA)
Publication Details
- Journal
- Digital Discovery
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1039/d5dd00018a
- Citations
- 10
- Primary Topic
- Statistics Education and Methodologies
- Type
- article
- Field-Weighted Citation Impact
- 26.30
Funders
- Canadian Institute for Advanced Research
- Government of Ontario
- Government of Canada
- University of Toronto
- Canada First Research Excellence Fund
- Natural Sciences and Engineering Research Council of Canada
- Office of Naval Research