Abstract
Recent advances in high-throughput (HTP) computational power and machine learning have led to great achievements in exploration of new thermoelectric materials. However, experimental discovery and optimization of thermoelectric materials have long relied on the traditional Edisonian trial and error approach. Herein, we demonstrate that ultrahigh thermoelectric performance in a Cu-doped PbSe-PbS system can be realized by HTP experimental screening and precise property modulation. Combining the HTP experimental technique with transport model analysis, an optimal Se/S ratio showing high thermoelectric performance has been efficiently screened out. Subsequently, based on the screened Se/S ratio, the doping content of Cu has been subtly adjusted to reach the optimum carrier concentration. As a result, an outstanding peak zT~1.6 is achieved at 873 K for a 1.8 at% Cu-doped PbSe0.6S0.4 sample, which is the superior value among the n-type Te-free lead chalcogenides. We anticipate that current work will stimulate large-scale unitization of the HTP experimental technique in the thermoelectric field, which can greatly accelerate the research and development of new high-performance thermoelectric materials.
Highlights
In 2011, the Obama administration launched the “Materials Genome Initiative” (MGI) project, which was aimed at reducing the cost and shortening the research and development cycle for exploring new materials [1]
An outstanding peak zT~1:6 at 873 K for a 1.8 at% Cu-doped PbSe0.6S0.4 sample is achieved, which is a superior value among the n-type Te-free lead chalcogenides
The probably optimized Se/S ratio is determined through combining the results of step (2) and step (3) for further thermoelectric performance optimization
Summary
In 2011, the Obama administration launched the “Materials Genome Initiative” (MGI) project, which was aimed at reducing the cost and shortening the research and development cycle for exploring new materials [1]. Recent advances in computational power and machine learning have led to great achievements in theoretical prediction of new functional materials in the fields of catalysis [2], lithium battery [3], photovoltaics [4], and thermoelectrics [5,6,7,8,9]. Discovery and optimization of functional materials have long depended on the traditional Edisonian trial and error approach, which leads to costly and time-consuming procedures in verifying the massive theoretical HTP results [11]. It is of great theoretical and practical significance to exploit experimental HTP techniques, which may bring about revolutionary breakthrough in material research
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