Experimental Ni₃TeO₆ synthesis condition exploration accelerated by active learning
Botella, R.; Fernández-Catalá, J.; Cao, W. (2023-09-12)
R. Botella, J. Fernández-Catalá, W. Cao, Experimental Ni3TeO6 synthesis condition exploration accelerated by active learning, Materials Letters, Volume 352, 2023, 135070, ISSN 0167-577X, https://doi.org/10.1016/j.matlet.2023.135070
© 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
https://creativecommons.org/licenses/by/4.0/
https://urn.fi/URN:NBN:fi-fe20231010139427
Tiivistelmä
Abstract
Material synthesis is time- and chemicals-consuming due to the traditional (“brute force”) methodology. For instance, Ni₃TeO₆ (NTO) is a multiferroic material relevant in different applications. Herein, we used an active learning scheme to explore the different phases obtained using a complex hydrothermal synthesis procedure instead of a solid-state methodology. Different from conventional ML prediction requiring a large dataset, we show that with only 9 data points obtained through experimental endeavor, 87% of the experimental condition space is predicted. The predicted phase configuration is verified with the sample in a new synthetic work. Beside exploring the NTO species, scheme developed herein constitute a powerful tool for experimental condition optimization.
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