Integrating mechanistic modelling with Bayesian optimisation: accelerated self-driving laboratories for RAFT polymerisation
Here, we have developed an accelerated self-driving laboratory that utilises a computationally inexpensive model to inform multi-objective Bayesian optimisation, to further minimise experimental overheads for precision RAFT polymerisation.
Authors
- Richard A. Bourne (ORCID: https://orcid.org/0000-0001-7107-6297)
- Clarissa. Y. P. Wilding (ORCID: https://orcid.org/0000-0002-2441-9741)
- Nicholas J. Warren (ORCID: https://orcid.org/0000-0002-8298-1417)
Institutions
- University of Leeds (GB)
- University of Sheffield (GB)
Publication Details
- Journal
- Digital Discovery
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1039/d5dd00258c
- Citations
- 3
- Primary Topic
- Innovative Microfluidic and Catalytic Techniques Innovation
- Type
- article
- Field-Weighted Citation Impact
- 2.02
Funders
- Royal Academy of Engineering
- University of Leeds
- Engineering and Physical Sciences Research Council