Identifying Highly Deformable van der Waals Layered Chalcogenides with Superior Thermoelectric Performance Using Deformability Factors and Interpretable Machine Learning
Qi Ren, Yingzhuo Lun, Bonan Zhu, Gang Tang, Jiawang Hong

TL;DR
This study uses machine learning and deformability factors to efficiently identify highly deformable van der Waals layered chalcogenides with superior thermoelectric performance, discovering promising candidates like NbSe2Br2.
Contribution
Introduces a high-throughput screening method combining deformability factors and machine learning to find flexible thermoelectric materials with high performance.
Findings
NbSe2Br2 achieved ZTmax of 1.35 at 1000K
Discovered materials surpassing organic thermoelectrics in power factor
Validated candidate materials with first principles calculations
Abstract
Van der Waals layered chalcogenide-based flexible thermoelectric devices show great potential for applications in wearable electronics. However, materials that are both highly deformable and exhibit superior thermoelectric performance are extremely limited. There is an urgent need for methods that can efficiently predict both deformability and thermoelectric performance to enable high-throughput screening of these materials. In this study, over 1000 van der Waals layered chalcogenides were high-throughput screened from material databases, the deformability of which were predicted with our previously developed deformability factor. An accurate and efficient model based on machine learning methods were developed to predict the thermoelectric properties. Several candidate materials with both deformability and thermoelectric potential were successfully discovered. Among them, NbSe2Br2 was…
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Taxonomy
TopicsAdvanced Thermoelectric Materials and Devices · 2D Materials and Applications · Machine Learning in Materials Science
