Machine learning guided appraisal and exploration of phase design for high entropy alloys
Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
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Original language | English |
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Article number | 128 |
Journal / Publication | npj Computational Materials |
Volume | 5 |
Online published | 20 Dec 2019 |
Publication status | Published - 2019 |
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DOI | DOI |
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Link to Scopus | https://www.scopus.com/record/display.uri?eid=2-s2.0-85076886937&origin=recordpage |
Permanent Link | https://scholars.cityu.edu.hk/en/publications/publication(ba189717-59ac-4176-b0fd-fa186a4fbe6c).html |
Abstract
High entropy alloys (HEAs) and compositionally complex alloys (CCAs) have recently attracted great research interest because of their remarkable mechanical and physical properties. Although many useful HEAs or CCAs were reported, the rules of phase design, if there are any, which could guide alloy screening are still an open issue. In this work, we made a critical appraisal of the existing design rules commonly used by the academic community with different machine learning (ML) algorithms. Based on the artificial neural network algorithm, we were able to derive and extract a sensitivity matrix from the ML modeling, which enabled the quantitative assessment of how to tune a design parameter for the formation of a certain phase, such as solid solution, intermetallic, or amorphous phase. Furthermore, we explored the use of an extended set of new design parameters, which had not been considered before, for phase design in HEAs or CCAs with the ML modeling. To verify our ML-guided design rule, we performed various experiments and designed a series of alloys out of the Fe-Cr-Ni-Zr-Cu system. The outcomes of our experiments agree reasonably well with our predictions, which suggests that the ML-based techniques could be a useful tool in the future design of HEAs or CCAs.
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Citation Format(s)
Machine learning guided appraisal and exploration of phase design for high entropy alloys. / Zhou, Ziqing; Zhou, Yeju; He, Quanfeng et al.
In: npj Computational Materials, Vol. 5, 128, 2019.
In: npj Computational Materials, Vol. 5, 128, 2019.
Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
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