Research framework of accelerating the exploration of i-TE materials (IMAGE)
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I-TE materials experience uneven anion and cation diffusion under temperature gradients, generating an electrical potential difference to power external devices. A ML regression model is trained with the features to evaluate the Seebeck coefficient accurately. This model enables direct screening of i-TE materials. Furthermore, interpretable analysis of the ML model can provide valuable physical insights, fostering the advancement of high-performance i-TE materials from a more intuitive perspective.
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