Thermoelectric technologies are becoming indispensable in the quest for a sustainable future. Recently, an emerging phenomenon, the spin-driven thermoelectric effect (STE), has garnered much attention as a promising path towards low cost and versatile thermoelectric technology with easily scalable manufacturing. However, progress in development of STE devices is hindered by the lack of understanding of the fundamental physics and materials properties responsible for the effect. In such nascent scientific field, data-driven approaches relying on statistics and machine learning, instead of more traditional modeling methods, can exhibit their full potential. Here, we use machine learning modeling to establish the key physical parameters controlling STE. Guided by the models, we have carried out actual material synthesis which led to the identification of a novel STE material with a thermopower an order of magnitude larger than that of the current generation of STE devices.

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Photoluminescence spectra in the near-band-gap region of Si1−xGex alloys (x=0.04 and 0.15) grown on Si(100) substrates by molecular beam epitaxy have been measured at 4.2 and 12 K. Radiative recombinations of free and bound excitons in thin layers of Si1−xGex alloys have been clearly observed for the first time. No-phonon transitions and transverse-optical (TO) phonon-assisted transitions have been identified.The luminescence lines become broader with an increase in excitation intensity; the broadening is interpreted to be due to the generation of the bound multiexciton complexes (BMECs). The position of the band-edge luminescence lines is determined by the strain in the epitaxial layer as well as the alloy composition. The defect-related L band appears in the case of x=0.15.

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