Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Autonomous search for materials with high Curie temperature using ab initio calculations and machine learning
Efficient exploration of vast material spaces is a challenging task in materials science. Autonomous material search methods utilizing machine learning and ab initio calculations have emerged as powerful alternatives to traditional material discovery through synthesis and analysis, which is time-consuming and scope-limited. Although autonomous search methods have already been applied to various material spaces, they have not explored the extensive material space of Curie temperatures. Herein, we show a simulation-based autonomous search method that suggests ternary alloys with high Curie temperatures. The material space – consisting of disordered ternary magnetic alloys – is explored through Korringa – Kohn – Rostoker coherent potential approximation and Bayesian optimization. Over a continuous 10-day search, the system proposed several alloys – CoAuIr, CoPtPd, and CoFeBi – with Curie temperatures surpassing that of pure face-centered cubic Co. Although the insights gained through these predictions require further experimental and theoretical validation, this study demonstrates that autonomous material search methods can potentially accelerate material discovery and optimize material properties.
Autonomous search for materials with high Curie temperature using ab initio calculations and machine learning
Efficient exploration of vast material spaces is a challenging task in materials science. Autonomous material search methods utilizing machine learning and ab initio calculations have emerged as powerful alternatives to traditional material discovery through synthesis and analysis, which is time-consuming and scope-limited. Although autonomous search methods have already been applied to various material spaces, they have not explored the extensive material space of Curie temperatures. Herein, we show a simulation-based autonomous search method that suggests ternary alloys with high Curie temperatures. The material space – consisting of disordered ternary magnetic alloys – is explored through Korringa – Kohn – Rostoker coherent potential approximation and Bayesian optimization. Over a continuous 10-day search, the system proposed several alloys – CoAuIr, CoPtPd, and CoFeBi – with Curie temperatures surpassing that of pure face-centered cubic Co. Although the insights gained through these predictions require further experimental and theoretical validation, this study demonstrates that autonomous material search methods can potentially accelerate material discovery and optimize material properties.
Autonomous search for materials with high Curie temperature using ab initio calculations and machine learning
Yuma Iwasaki (Autor:in)
2024
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
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| British Library Online Contents | 2018
| British Library Online Contents | 2018
| British Library Online Contents | 2018
Ferroelectric ceramics with high curie temperature
| Engineering Index Backfile | 1958