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Title: Thermoelectric properties of TaVO 5 and GdTaO 4 : An experimental verification of machine learning prediction
Advancements in materials discovery tend to rely disproportionately on happenstance and luck rather than employing a systematic approach. Recently, advances in computational power have allowed researchers to build computer models to predict the material properties of any chemical formula. From energy minimization techniques to machine learning-based models, these algorithms have unique strengths and weaknesses. However, a computational model is only as good as its accuracy when compared to real-world measurements. In this work, we take two recommendations from a thermoelectric machine learning model, TaVO[Formula: see text] and GdTaO[Formula: see text], and measure their thermoelectric properties of Seebeck coefficient, thermal conductivity, and electrical conductivity. We see that the predictions are mixed; thermal conductivities are correctly predicted, while electrical conductivities and Seebeck coefficients are not. Furthermore, we explore TaVO[Formula: see text]’s unusually low thermal conductivity of 1.2 Wm[Formula: see text]K[Formula: see text], and we discover a possible new avenue of research of a low thermal conductivity oxide family.  more » « less
Award ID(s):
1651668
PAR ID:
10500966
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
Advances in Applied Ceramics: Structural, Functional and Bioceramics
Date Published:
Journal Name:
Advances in Applied Ceramics: Structural, Functional and Bioceramics
ISSN:
1743-6753
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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