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Unveiling the physical mechanisms driving delafossite crystal (ABX2) formation through interpretable machine learning.
Xu, Ning; Li, Zheng; Fu, Xiaolan; Hu, Xiaojuan; Xu, Wenwu; Han, Zhong-Kang.
Afiliação
  • Xu N; Department of Physics, School of Physical Science and Technology, Ningbo University, Ningbo, 315211, China. xuwenwu@nbu.edu.cn.
  • Li Z; Department of Physics, School of Physical Science and Technology, Ningbo University, Ningbo, 315211, China. xuwenwu@nbu.edu.cn.
  • Fu X; Department of Physics, School of Physical Science and Technology, Ningbo University, Ningbo, 315211, China. xuwenwu@nbu.edu.cn.
  • Hu X; Fritz-Haber-Institut der Max-Planck-Gesellschaft, Faradayweg 4-6, 14195 Berlin, Germany. xhu@fhi-berlin.mpg.de.
  • Xu W; School of Materials Science and Engineering, Zhejiang University, Hangzhou, 310027, China. hanzk@zju.edu.cn.
  • Han ZK; Department of Physics, School of Physical Science and Technology, Ningbo University, Ningbo, 315211, China. xuwenwu@nbu.edu.cn.
Chem Commun (Camb) ; 60(49): 6324-6327, 2024 Jun 13.
Article em En | MEDLINE | ID: mdl-38826149
ABSTRACT
A method integrating machine learning with first-principles calculations is employed to forecast the formation energy of delafossite crystals, facilitating the rapid identification of stable crystals. This approach identifies several stable candidates and highlights the importance of atomic ionization energy and electron affinity in the formation of delafossite crystals.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article
Texto completo
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XML
PubMed Links
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article