論文

2021年12月

Linear-superelastic Ti-Nb nanocomposite alloys with ultralow modulus via high-throughput phase-field design and machine learning

npj Computational Materials
  • Yuquan Zhu
  • ,
  • Tao Xu
  • ,
  • Qinghua Wei
  • ,
  • Jiawei Mai
  • ,
  • Hongxin Yang
  • ,
  • Huiran Zhang
  • ,
  • Takahiro Shimada
  • ,
  • Takayuki Kitamura
  • ,
  • Tong-Yi Zhang

7
1
記述言語
掲載種別
研究論文(学術雑誌)
DOI
10.1038/s41524-021-00674-7
出版者・発行元
Springer Science and Business Media LLC

<title>Abstract</title>The optimal design of shape memory alloys (SMAs) with specific properties is crucial for the innovative application in advanced technologies. Herein, inspired by the recently proposed design concept of concentration modulation, we explore martensitic transformation (MT) in and design the mechanical properties of Ti-Nb nanocomposites by combining high-throughput phase-field simulations and machine learning (ML) approaches. Systematic phase-field simulations generate data of the mechanical properties for various nanocomposites constructed by four macroscopic degrees of freedom. An ML-assisted strategy is adopted to perform multiobjective optimization of the mechanical properties, through which promising nanocomposite configurations are prescreened for the next set of phase-field simulations. The ML-guided simulations discover an optimized nanocomposite, composed of Nb-rich matrix and Nb-lean nanofillers, that exhibits a combination of mechanical properties, including ultralow modulus, linear super-elasticity, and near-hysteresis-free in a loading-unloading cycle. The exceptional mechanical properties in the nanocomposite originate from optimized continuous MT rather than a sharp first-order transition, which is common in typical SMAs. This work demonstrates the great potential of ML-guided phase-field simulations in the design of advanced materials with extraordinary properties.

リンク情報
DOI
https://doi.org/10.1038/s41524-021-00674-7
URL
https://www.nature.com/articles/s41524-021-00674-7.pdf
URL
https://www.nature.com/articles/s41524-021-00674-7
ID情報
  • DOI : 10.1038/s41524-021-00674-7
  • eISSN : 2057-3960

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