[HTML][HTML] Experimental ni3teo6 synthesis condition exploration accelerated by active learning
Materials Letters, 2023•Elsevier
Material synthesis is time-and chemicals-consuming due to the traditional (“brute force”)
methodology. For instance, Ni 3 TeO 6 (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 …
methodology. For instance, Ni 3 TeO 6 (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 …
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.
Elsevier
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