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Discovery of novel quaternary bulk metallic glasses using a developed correlation-based neural network approach
, Article Computational Materials Science ; Volume 186 , 2021 ; 09270256 (ISSN) ; Gholamipour, R ; Samavatian, V ; Sharif University of Technology
Elsevier B.V
2021
Abstract
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...