Machine learning predictions of low thermal conductivity: comparing TaVO5 and GdTaO4

15 March 2023, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


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, TaVO5 and GdTaO4, and test 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 discover a possible new avenue of research of a low thermal conductivity oxide family.


materials informatics
thermal conductivity


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