Data analytics accelerates the experimental discovery of new thermoelectric materials with extremely high figure of merit

S. Levchenko, Yaqiong Zhong, Xiaojuan Hu, Debalaya Sarker, Qingrui Xia, Liangliang Xu, Chao Yang, Zhongkang Han, J. Cui
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引用次数: 1

Abstract

Thermoelectric (TE) materials are among very few sustainable yet feasible energy solutions of present time. This huge promise of energy harvesting is contingent on identifying/designing materials having higher efficiency than presently available ones. However, due to the vastness of the chemical space of materials, only its small fraction was scanned experimentally and/or computationally so far. Employing a compressed-sensing based symbolic regression in an active-learning framework, we have not only identified a trend in materials’ compositions for superior TE performance, but have also predicted and experimentally synthesized several extremely high performing novel TE materials. Among these, we found polycrystalline p-type Cu0.45Ag0.55GaTe2 to possess an experimental figure of merit as high as ~2.8 at 827 K. This is a breakthrough in the field, because all previously known thermoelectric materials with a comparable figure of merit are either unstable or much more difficult to synthesize, rendering them unusable in large-scale applications. The presented methodology demonstrates the importance and tremendous potential of physically informed descriptors in material science, in particular for relatively small data sets typically available from experiments at well-controlled conditions.
数据分析加速了具有极高价值的新型热电材料的实验发现
热电材料是目前为数不多的可持续但可行的能源解决方案之一。这种能量收集的巨大前景取决于识别/设计比现有材料效率更高的材料。然而,由于材料的化学空间很大,迄今为止,只有很小一部分被实验和/或计算扫描。在主动学习框架中采用基于压缩感知的符号回归,我们不仅确定了材料成分具有优越TE性能的趋势,而且还预测并实验合成了几种高性能的新型TE材料。其中,我们发现多晶p型Cu0.45Ag0.55GaTe2在827 K时具有高达~2.8的实验优值。这是该领域的一个突破,因为所有已知的具有类似性能的热电材料要么不稳定,要么难以合成,这使得它们无法在大规模应用中使用。所提出的方法证明了物理信息描述符在材料科学中的重要性和巨大潜力,特别是对于通常在良好控制条件下从实验中获得的相对较小的数据集。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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