A NIMS-University of Tokyo research group has succeeded in greatly enhancing the power output of thermoelectric materials composed of abundant elements—aluminum, iron, and silicon—using machine learning scheme. Machine learning prediction enabled the group to discover optimum chemical compositions, which the conventional experimental approach had failed to detect, resulting in a 40% increase in power output. Machine learning may dramatically accelerate R&D of thermoelectric materials composed only of abundant elements toward practical use.

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Zhufeng Hou*, Yoshiki Takagiwa*, Yoshikazu Shinohara, Yibin Xu, Koji Tsuda, ACS Appl. Mater. Interfaces 11, 11545–11554 (2019). "Machine-Learning-Assisted Development and Theoretical Consideration for the Al2Fe3Si3 Thermoelectric Material"
DOI: 10.1021/acsami.9b02381