Efficient global crystal structure prediction using polynomial machine learning potential in the binary Al–Cu alloy system

Machine learning potentials (MLPs) are attracting much attention as powerful tools to accurately and efficiently perform atomistic simulations and crystal structure predictions. In this study, we develop a polynomial MLP for the Al–Cu system applicable to the robust global structure search and metas...

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Veröffentlicht in:	Journal of the Ceramic Society of Japan 2023/10/01, Vol.131(10), pp.762-766
Hauptverfasser:	Wakai, Hayato, Seko, Atsuto, Tanaka, Isao
Format:	Artikel
Sprache:	eng ; jpn
Schlagworte:	Alloy system Alloy systems Aluminum base alloys Binary alloys Crystal structure Crystal structure prediction Density functional theory (DFT) calculation Enumeration Global optimization Machine learning Machine learning potentials Polynomials
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