Unsupervised machine learning for discovery of promising half-Heusler thermoelectric materials

Xue Jia; Yanshuai Deng; Xin Bao; Honghao Yao; Shan Li; Zhou Li; Chen Chen; Xinyu Wang; Jun Mao; Feng Cao; Jiehe Sui; Junwei Wu; Cuiping Wang; Qian Zhang; Xingjun Liu

doi:10.1038/s41524-022-00723-9

Unsupervised machine learning for discovery of promising half-Heusler thermoelectric materials

Xue Jia
, Yanshuai Deng
, Xin Bao
, Honghao Yao
, Shan Li
, Zhou Li
, Chen Chen
, Xinyu Wang
, Jun Mao
, Feng Cao
, Jiehe Sui
, Junwei Wu
, Cuiping Wang
, Qian Zhang
, Xingjun Liu

School of Materials Science and Engineering, Harbin Institute of Technology
School of Science, Harbin Institute of Technology Shenzhen
Harbin Institute of Technology
Xiamen University

科研成果: 期刊稿件 › 文章 › 同行评审

78 引用（Scopus）

摘要

Thermoelectric materials can be potentially applied to waste heat recovery and solid-state cooling because they allow a direct energy conversion between heat and electricity and vice versa. The accelerated materials design based on machine learning has enabled the systematic discovery of promising materials. Herein we proposed a successful strategy to discover and design a series of promising half-Heusler thermoelectric materials through the iterative combination of unsupervised machine learning with the labeled known half-Heusler thermoelectric materials. Subsequently, optimized zT values of ~0.5 at 925 K for p-type Sc_0.7Y_0.3NiSb_0.97Sn_0.03 and ~0.3 at 778 K for n-type Sc_0.65Y_0.3Ti_0.05NiSb were experimentally achieved on the same parent ScNiSb.

源语言	英语
文章编号	34
期刊	npj Computational Materials
卷	8
期	1
DOI	https://doi.org/10.1038/s41524-022-00723-9
出版状态	已出版 - 12月 2022
已对外发布	是

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abstract = "Thermoelectric materials can be potentially applied to waste heat recovery and solid-state cooling because they allow a direct energy conversion between heat and electricity and vice versa. The accelerated materials design based on machine learning has enabled the systematic discovery of promising materials. Herein we proposed a successful strategy to discover and design a series of promising half-Heusler thermoelectric materials through the iterative combination of unsupervised machine learning with the labeled known half-Heusler thermoelectric materials. Subsequently, optimized zT values of \textasciitilde{}0.5 at 925 K for p-type Sc0.7Y0.3NiSb0.97Sn0.03 and \textasciitilde{}0.3 at 778 K for n-type Sc0.65Y0.3Ti0.05NiSb were experimentally achieved on the same parent ScNiSb.",

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doi = "10.1038/s41524-022-00723-9",

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T1 - Unsupervised machine learning for discovery of promising half-Heusler thermoelectric materials

AU - Jia, Xue

AU - Deng, Yanshuai

AU - Bao, Xin

AU - Yao, Honghao

AU - Li, Shan

AU - Li, Zhou

AU - Chen, Chen

AU - Wang, Xinyu

AU - Mao, Jun

AU - Cao, Feng

AU - Sui, Jiehe

AU - Wu, Junwei

AU - Wang, Cuiping

AU - Zhang, Qian

AU - Liu, Xingjun

PY - 2022/12

Y1 - 2022/12

N2 - Thermoelectric materials can be potentially applied to waste heat recovery and solid-state cooling because they allow a direct energy conversion between heat and electricity and vice versa. The accelerated materials design based on machine learning has enabled the systematic discovery of promising materials. Herein we proposed a successful strategy to discover and design a series of promising half-Heusler thermoelectric materials through the iterative combination of unsupervised machine learning with the labeled known half-Heusler thermoelectric materials. Subsequently, optimized zT values of ~0.5 at 925 K for p-type Sc0.7Y0.3NiSb0.97Sn0.03 and ~0.3 at 778 K for n-type Sc0.65Y0.3Ti0.05NiSb were experimentally achieved on the same parent ScNiSb.

AB - Thermoelectric materials can be potentially applied to waste heat recovery and solid-state cooling because they allow a direct energy conversion between heat and electricity and vice versa. The accelerated materials design based on machine learning has enabled the systematic discovery of promising materials. Herein we proposed a successful strategy to discover and design a series of promising half-Heusler thermoelectric materials through the iterative combination of unsupervised machine learning with the labeled known half-Heusler thermoelectric materials. Subsequently, optimized zT values of ~0.5 at 925 K for p-type Sc0.7Y0.3NiSb0.97Sn0.03 and ~0.3 at 778 K for n-type Sc0.65Y0.3Ti0.05NiSb were experimentally achieved on the same parent ScNiSb.

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Unsupervised machine learning for discovery of promising half-Heusler thermoelectric materials

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