Zobrazeno 1 - 10 of 59 pro vyhledávání: '"Jinyang Xi"'
1
Akademický článek
The MatHub‐3d first‐principles repository and the applications on thermoelectrics
Autor: Lu Liu, Mingjia Yao, Yuxiang Wang, Yeqing Jin, Jialin Ji, Huifang Luo, Yan Cao, Yifei Xiong, Ye Sheng, Xin Li, Di Qiu, Lili Xi, Jinyang Xi, Wenqing Zhang, Lidong Chen, Jiong Yang
Publikováno v: Materials Genome Engineering Advances, Vol 2, Iss 1, Pp n/a-n/a (2024)
Abstract Following the Materials Genome Initiative project, materials research has embarked a new research paradigm centered around material repositories, significantly accelerating the discovery of novel materials, such as thermoelectrics. Thermoele
Externí odkaz: https://doaj.org/article/07f98c326f95407d8d6be7dd016e4fd2
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2
Akademický článek
High-throughput deformation potential and electrical transport calculations
Autor: Yeqing Jin, Xiangdong Wang, Mingjia Yao, Di Qiu, David J. Singh, Jinyang Xi, Jiong Yang, Lili Xi
Publikováno v: npj Computational Materials, Vol 9, Iss 1, Pp 1-8 (2023)
Abstract The deformation potential plays an important role in electrical transport properties, but in the context of high-throughput searches requires a consistent and readily computable reference level. Here, we design a high-throughput method for c
Externí odkaz: https://doaj.org/article/3b2213e359a048b7bb72c03e92e5855b
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3
Akademický článek
Intrinsic defects and the influences on electrical transport properties in quaternary diamond-like compounds: Cd2Cu3In3Te8 as an example
Autor: Jianxin Zhang, Zhou Zhang, Lili Xi, Jinyang Xi, Jiong Yang
Publikováno v: Journal of Materiomics, Vol 8, Iss 6, Pp 1222-1229 (2022)
Over the years, the fact that the quaternary diamond-like thermoelectric materials show much lower carrier mobilities than ternary compounds remains mysterious. In this work, by adopting first-principles defect chemistry and electrical transport calc
Externí odkaz: https://doaj.org/article/7522bb1fcf0a4d49987a1f439bb2fc55
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4
Akademický článek
Accurate and explainable machine learning for the power factors of diamond-like thermoelectric materials
Autor: Zhe Yang, Ye Sheng, Cong Zhu, Jianyue Ni, Zhenyu Zhu, Jinyang Xi, Wu Zhang, Jiong Yang
Publikováno v: Journal of Materiomics, Vol 8, Iss 3, Pp 633-639 (2022)
The application of machine learning (ML)-based methods to the study of thermoelectric (TE) materials is promising. Although conventional ML algorithms can achieve high prediction performance, their lack of interpretability severely obstructs research
Externí odkaz: https://doaj.org/article/57ec4f76e79a494185a40e6bff37124b
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5
Akademický článek
Thermoelectric transport properties in chalcogenides ZnX (X=S, Se): From the role of electron-phonon couplings
Autor: Jincheng Ding, Changdong Liu, Lili Xi, Jinyang Xi, Jiong Yang
Publikováno v: Journal of Materiomics, Vol 7, Iss 2, Pp 310-319 (2021)
Electron-phonon coupling (EPC) is a key factor for thermoelectric properties of materials. In this paper, the thermoelectric properties of zinc-blende chalcogenides (p-type) ZnS and ZnSe have been studied through full evaluation of EPC from first-pri
Externí odkaz: https://doaj.org/article/0e0ef57bcde24ac09c10e587e9a4d2a6
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7
Functional-Unit-Based Material Design: Ultralow Thermal Conductivity in Thermoelectrics with Linear Triatomic Resonant Bonds
Autor: Jialin Ji, Qinghang Tang, Mingjia Yao, Hongliang Yang, Yeqing Jin, Yubo Zhang, Jinyang Xi, David J. Singh, Jiong Yang, Wenqing Zhang
Publikováno v: Journal of the American Chemical Society. 144:18552-18561
We demonstrate the use of functional-unit-based material design for thermoelectrics. This is an efficient approach for identifying high-performance thermoelectric materials, based on the use of combinations of functional fragments relevant to desired
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c09a349eafe52f1e73d9b9c079dc3d4e
https://doi.org/10.1021/jacs.2c08062
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10
Accurate and explainable machine learning for the power factors of diamond-like thermoelectric materials
Autor: Jiong Yang, Jinyang Xi, Jianyue Ni, Zhe Yang, Zhenyu Zhu, Cong Zhu, Ye Sheng, Wu Zhang
Publikováno v: Journal of Materiomics. 8:633-639
The application of machine learning (ML)-based methods to the study of thermoelectric (TE) materials is promising. Although conventional ML algorithms can achieve high prediction performance, their lack of interpretability severely obstructs research
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::dcd950e2a62c260cb2989a0094516162
https://doi.org/10.1016/j.jmat.2021.11.010
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