DOE OSTI · 2537962
Measurements with noise: Bayesian optimization for co-optimizing noise and property discovery in automated experiments
Abstract
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.
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Slautin, Boris N. [Institute for Materials Science and Center for Nanointegration Duisburg-Essen (CENIDE), University of Duisburg-Essen, Essen, 45141, Germany] (ORCID:0000000212377070), Liu, Yu [Department of Materials Science and Engineering, University of Tennessee, Knoxville, TN 37996, USA], Dec, Jan [Institute of Materials Science, University of Silesia, Katowice, PL 40-007, Poland], Shvartsman, Vladimir V. [Institute for Materials Science and Center for Nanointegration Duisburg-Essen (CENIDE), University of Duisburg-Essen, Essen, 45141, Germany] (ORCID:0000000271552473), Lupascu, Doru C. [Institute for Materials Science and Center for Nanointegration Duisburg-Essen (CENIDE), University of Duisburg-Essen, Essen, 45141, Germany] (ORCID:0000000268951334), Ziatdinov, Maxim A. [Pacific Northwest National Laboratory, Richland, WA 99354, USA] (ORCID:0000000325704592), Kalinin, Sergei V. [Department of Materials Science and Engineering, University of Tennessee, Knoxville, TN 37996, USA, Pacific Northwest National Laboratory, Richland, WA 99354, USA] (ORCID:0000000153546152). 2025-01-01. Measurements with noise: Bayesian optimization for co-optimizing noise and property discovery in automated experiments. https://doi.org/10.1039/d4dd00391h
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