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

被引：38

作者：

Jia, Xue ^{[1
,2
]}

Deng, Yanshuai ^{[1
,2
]}

Bao, Xin ^{[1
,2
]}

Yao, Honghao ^{[1
,2
]}

Li, Shan ^{[1
,2
]}

Li, Zhou ^{[1
,2
]}

Chen, Chen ^{[1
,2
]}

Wang, Xinyu ^{[1
,2
]}

Mao, Jun ^{[1
,2
]}

Cao, Feng ^{[3
]}

Sui, Jiehe ^{[4
]}

Wu, Junwei ^{[1
,2
]}

Wang, Cuiping ^{[1
,2
,5
]}

Zhang, Qian ^{[1
,2
,4
]}

Liu, Xingjun ^{[1
,2
,4
,5
]}

机构：

[1] Harbin Inst Technol, Sch Mat Sci & Engn, Shenzhen 518055, Peoples R China

[2] Harbin Inst Technol, Inst Mat Genome & Big Data, Shenzhen 518055, Peoples R China

[3] Harbin Inst Technol, Sch Sci, Shenzhen 518055, Peoples R China

[4] Harbin Inst Technol, State Key Lab Adv Welding & Joining, Harbin 150001, Peoples R China

[5] Xiamen Univ, Coll Mat, Dept Mat Sci & Engn, Xiamen 361005, Peoples R China

来源：

NPJ COMPUTATIONAL MATERIALS | 2022年 / 8卷 / 01期

基金：

国家重点研发计划; 中国国家自然科学基金;

关键词：

PERFORMANCE; MECHANISMS; SC; HF;

D O I：

10.1038/s41524-022-00723-9

中图分类号：

O64 [物理化学（理论化学）、化学物理学];

学科分类号：

070304 ; 081704 ;

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 similar to 0.5 at 925 K for p-type Sc0.7Y0.3NiSb0.97Sn0.03 and similar to 0.3 at 778 K for n-type Sc0.65Y0.3Ti0.05NiSb were experimentally achieved on the same parent ScNiSb.

引用

页数：9

共 50 条

[1] 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
[J]. npj Computational Materials, 8
[2] Discovery of YbNiSb-Based Half-Heusler Alloys as Promising Thermoelectric Materials
Huang, Jiamian
Liu, Rongtao
Ma, Quanying
Jiang, Zuojian
Jiang, Yibin
Li, Yu
Wang, Chenyang
[J]. ACS APPLIED ENERGY MATERIALS, 2022, 5 (10) : 12630 - 12639
[3] Half-Heusler thermoelectric materials
Xia, Kaiyang
Hu, Chaoliang
Fu, Chenguang
Zhao, Xinbing
Zhu, Tiejun
[J]. APPLIED PHYSICS LETTERS, 2021, 118 (14)
[4] Machine Learning Chemical Guidelines for Engineering Electronic Structures in Half-Heusler Thermoelectric Materials
Dylla, Maxwell T.
Dunn, Alexander
Anand, Shashwat
Jain, Anubhav
Snyder, G. Jeffrey
[J]. RESEARCH, 2020, 2020
[5] Half-Heusler thermoelectric materials: NMR studies
Tian, Yefan
Ghassemi, Nader
Ren, Wuyang
Zhu, Hangtian
Li, Shan
Zhang, Qian
Wang, Zhiming
Ren, Zhifeng
Ross, Joseph H., Jr.
[J]. JOURNAL OF APPLIED PHYSICS, 2020, 128 (05)
[6] New thermoelectric materials with half-Heusler structure
Huang, XY
Xu, Z
Chen, LD
[J]. JOURNAL OF INORGANIC MATERIALS, 2004, 19 (01) : 25 - 30
[7] Recent progress in half-Heusler thermoelectric materials
Huang, Lihong
Zhang, Qinyong
Yuan, Bo
Lai, Xiang
Yan, Xiao
Ren, Zhifeng
[J]. MATERIALS RESEARCH BULLETIN, 2016, 76 : 107 - 112
[8] Antimonides with the half-Heusler structure: New thermoelectric materials
Department of Chemistry, Princeton Materials Institute, Princeton University, Princeton, NJ 08540, United States
不详
[J]. Appl Phys Lett, 10 (1415-1417):
[9] Selective Laser Melting of Half-Heusler Thermoelectric Materials
Zhang, Haidong
Wang, Shanyu
Taylor, Patrick J.
Yang, Jihui
LeBlanc, Saniya
[J]. ENERGY HARVESTING AND STORAGE: MATERIALS, DEVICES, AND APPLICATIONS VIII, 2018, 10663
[10] Zintl-chemistry for half-Heusler thermoelectric materials
Snyder, G. Jeffrey
[J]. ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, 2015, 250

← 1 2 3 4 5 →