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Creators/Authors contains: "Sparks, Taylor D."

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  1. 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. 
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  2. Prompted by limited available data, we explore data-aggregation strategies for material datasets, aiming to boost machine learning performance. Our findings suggest that intuitive aggregation schemes are ineffective in enhancing predictive accuracy. 
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  3. Diffusion Models outperform Generative Adversarial Networks (GANs) and Wasserstein GANs in material discovery. 
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