Predicting New Single/Multiphase-Structure High-Entropy Alloys Using a Pattern Recognition Network
Source abstract
Abstract
Machine learning methods were employed to predict the phase structures of high-entropy alloys (HEAs). These alloys were classified into four categories: bcc (body-centered cubic), fcc (face-centered cubic), bcc+fcc (body-centered cubic and face-centered cubic) and others (containing intermetallic compounds and other structural alloys). The utilized algorithm was a Pattern Recognition Network (PRN) utilizing cross-entropy as the loss function, enabling the prediction of HEAs’ phase formation probability. The PRN algorithm demonstrated an accuracy exceeding 87% based on the test data. The PRN algorithm successfully predicted the transformation from fcc to fcc+bcc and subsequently to a bcc structure with the increase in Al content in AlxCoCu6Ni6Fe6 and AlxCoCrCuNiFe HEAs. In addition, AlxCoCu6Ni6Fe6 (x = 1, 3, 6, 9) HEAs were prepared using a vacuum arc furnace, and the microstructure of the as-cast alloy was tested by means of XRD, SEM, and EBSD, confirming the high consistency between the predicted and observed phase structures. This study showcases the efficacy of the PRN algorithm in predicting both single- and multiphase-structure high-entropy alloys, offering valuable insights into alloy design and development.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: High Entropy Alloys Studies · High-Temperature Coating Behaviors · Additive Manufacturing Materials and Processes
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Materials Science and Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China
School of Materials Science and Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China
Shanxi Taigang Stainless Steel Precision Strip Co., Ltd., Taiyuan 030006, China
Funding
National Natural Science Foundation of China · Taiyuan University of Science and Technology Scientific Research Initial Funding · Excellent Dr. Laijin Research Funding
References (35)
Show source-provided references
1. A critical review of high entropy alloys and related concepts10.1016/j.actamat.2016.08.081 ↗
2. Accelerated exploration of multi-principal element alloys with solid solution phases10.1038/ncomms7529 ↗
3. Metastable high-entropy dual-phase alloys overcome the strength–ductility trade-off10.1038/nature17981 ↗
4. Mechanical properties of nbmotaw and vnbmotaw refractory high entropy alloys10.1016/j.intermet.2011.01.004 ↗
5. Ultrafine-grained alcocrfeni2.1 eutectic high-entropy alloy10.1080/21663831.2016.1160451 ↗
6. A fracture-resistant high-entropy alloy for cryogenic applications10.1126/science.1254581 ↗
7. A new lightweight Al2.7TiVCrCu high entropy alloy with excellent strength and toughness after homogenization treatment10.1016/j.msea.2023.144779 ↗
8. Novel tinbtazrmo high-entropy alloys for metallic biomaterials10.1016/j.scriptamat.2016.10.028 ↗
9. Development of non-equiatomic Ti-Nb-Ta-Zr-Mo high-entropy alloys for metallic biomaterials10.1016/j.scriptamat.2019.07.011 ↗
10. Investigation of mechanical and corrosion properties of light and high hardness cast AlTiVCrCu0.4 high entropy alloy10.1016/j.matchar.2023.112878 ↗
11. A new strategy to design eutectic high-entropy alloys using simple mixture method10.1016/j.matdes.2018.01.025 ↗
12. A new pseudo binary strategy to design eutectic high entropy alloys using mixing enthalpy and valence electron concentration10.1016/j.matdes.2018.01.057 ↗
13. Effects of carbon and molybdenum on the nanostructural evolution and strength/ductility trade-off in Fe40Mn40Co10Cr10 high-entropy alloys10.1016/j.jallcom.2022.165108 ↗
14. Design of a Seven-Component Eutectic High-Entropy Alloy10.1007/s11661-019-05210-3 ↗
15. Machine learning reveals the importance of the formation enthalpy and atom-size difference in forming phases of high entropy alloys10.1016/j.matdes.2020.108835 ↗
16. Machine learning-enabled identification of new medium to high entropy alloys with solid solution phases10.1016/j.commatsci.2021.110623 ↗
17. Discovery of aluminum alloys with ultra-strength and high-toughness via a property-oriented design strategy10.1016/j.jmst.2021.05.011 ↗
18. Machine learning approach to predict new multiphase high entropy alloys10.1016/j.scriptamat.2021.113804 ↗
19. Solid-Solution Phase Formation Rules for Multi-component Alloys10.1002/adem.200700240 ↗
20. Effect of valence electron concentration on stability of fcc or bcc phase in high entropy alloys10.1063/1.3587228 ↗
21. Prediction of high-entropy stabilized solid-solution in multi-component alloys10.1016/j.matchemphys.2011.11.021 ↗
22. Design of high entropy alloys: A single-parameter thermodynamic rule10.1016/j.scriptamat.2015.03.023 ↗
23. Machine-learning model for predicting phase formations of high-entropy alloys10.1103/PhysRevMaterials.3.095005 ↗
24. Machine Learning Based Prediction of Phases in High-Entropy Alloys10.1016/j.commatsci.2020.110244 ↗
25. Database on the mechanical properties of high entropy alloys and complex concentrated alloys10.1016/j.dib.2018.11.111 ↗
26. Deep learning-based phase prediction of high-entropy alloys: Optimization, generation, and explanation10.1016/j.matdes.2020.109260 ↗
27. Tailoring nanoprecipitates for ultra-strong high-entropy alloys via machine learning and prestrain aging10.1016/j.jmst.2020.07.009 ↗
28. Multi-label phase-prediction in high-entropy-alloys using Artificial-Neural-Network10.1016/j.matlet.2020.127606 ↗
29. A reverse design model for high-performance and low-cost magnesium alloys by machine learning10.1016/j.commatsci.2021.110881 ↗
30. Machine learning recommends affordable new Ti alloy with bone-like modulus10.1016/j.mattod.2019.08.008 ↗
31. Neural networks: An overview of early research, current frameworks and new challenges10.1016/j.neucom.2016.06.014 ↗
32. Artificial neural networks: Fundamentals, computing, design, and application10.1016/S0167-7012(00)00201-3 ↗
33. Alloying behavior in multi-component AlCoCrCuFe and NiCoCrCuFe high entropy alloys10.1016/j.msea.2011.11.044 ↗
34. Controlled formation of coherent cuboidal nanoprecipitates in body-centered cubic high-entropy alloys based on Al2(Ni,Co,Fe,Cr)14 compositions10.1016/j.actamat.2018.01.050 ↗
35. Nanostructured High-Entropy Alloys with Multiple Principal Elements: Novel Alloy Design Concepts and Outcomes10.1002/adem.200300567 ↗