Discovery of novel quaternary bulk metallic glasses using a developed correlation-based neural network approach

Samavatian, M ; Sharif University of Technology | 2021

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  1. Type of Document: Article
  2. DOI: 10.1016/j.commatsci.2020.110025
  3. Publisher: Elsevier B.V , 2021
  4. Abstract:
  5. The immense space of composition-processing parameters leads to numerous trial-and-error experimental works for engineering of novel bulk metallic glasses (BMGs). To tackle this challenging problem, it is required to consider specific guidelines which are able to restrict the productive alloying compositions. In this work, a correlation-based neural network (CBNN) approach was developed, based on a dataset of 7950 alloying compositions, to design potential new MGs through prediction of casting ability, reduced glass transition (Trg) and critical thickness (Dmax). This approach involves individual and mutual characteristics of contributory factors to improve the prediction accuracy. To validate our model, we selected the ZrCoAl alloying system and investigated the microalloying effects on the glass forming possibility (GFP). According to the results, the microalloying process effects strongly depended on the inherent features of added element. Moreover, the CBNN model predicted a quaternary system, i.e. ZrCoAlNi, in which the high GFP area was extended through a wide range of chemical compositions. Finally, it is concluded that the established framework offers a roadmap for potential applications in the development of new quaternary MG alloys. © 2020 Elsevier B.V
  6. Keywords:
  7. Glass ; Glass transition ; Metallic glass ; Microalloying ; Ternary alloys ; Alloying compositions ; Bulk metallic glass ; Chemical compositions ; Contributory factors ; Microalloying effects ; Prediction accuracy ; Processing parameters ; Quaternary systems ; Neural networks
  8. Source: Computational Materials Science ; Volume 186 , 2021 ; 09270256 (ISSN)
  9. URL: https://www.sciencedirect.com/science/article/abs/pii/S0927025620305164