Machine learning guided appraisal and exploration of phase design for high entropy alloys
| Autor: | Yong Yang, Fucheng Li, Ziqing Zhou, Yeju Zhou, Quanfeng He, Zhaoyi Ding |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
02 engineering and technology Machine learning computer.software_genre 01 natural sciences Set (abstract data type) Matrix (mathematics) Phase (matter) 0103 physical sciences lcsh:TA401-492 General Materials Science Sensitivity (control systems) lcsh:Computer software 010302 applied physics business.industry High entropy alloys 021001 nanoscience & nanotechnology Amorphous phase Computer Science Applications lcsh:QA76.75-76.765 Mechanics of Materials Modeling and Simulation Academic community lcsh:Materials of engineering and construction. Mechanics of materials Artificial intelligence 0210 nano-technology business Artificial neural network algorithm computer |
| Zdroj: | npj Computational Materials, Vol 5, Iss 1, Pp 1-9 (2019) |
| ISSN: | 2057-3960 |
| DOI: | 10.1038/s41524-019-0265-1 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|