Measurements with noise: Bayesian optimization for co-optimizing noise and property discovery in automated experiments
The proposed workflow integrates intra-step optimization into automated experiments, optimizing both the target property and measurement duration to enhance efficiency by balancing knowledge acquisition and experimental costs.
Authors
- Maxim Ziatdinov (ORCID: https://orcid.org/0000-0003-2570-4592)
- J. Dec (ORCID: https://orcid.org/0000-0003-4137-7018)
- Doru C. Lupascu (ORCID: https://orcid.org/0000-0002-6895-1334)
- Sergei V. Kalinin (ORCID: https://orcid.org/0000-0001-5354-6152)
- Vladimir V. Shvartsman (ORCID: https://orcid.org/0000-0002-7155-2473)
- Boris N. Slautin (ORCID: https://orcid.org/0000-0002-1237-7070)
- Yu Liu (ORCID: https://orcid.org/0000-0003-1337-2527)
Institutions
- Pacific Northwest National Laboratory (US)
- University of Duisburg-Essen (DE)
- University of Tennessee at Knoxville (US)
- University of Silesia in Katowice (PL)
Publication Details
- Journal
- Digital Discovery
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1039/d4dd00391h
- Citations
- 6
- Primary Topic
- Advanced Multi-Objective Optimization Algorithms
- Type
- article
- Field-Weighted Citation Impact
- 9.03