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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...
A model based on the equality of chemical potentials for describing the liquid-liquid interfaces of water-hydrocarbons up to high pressures
, Article Journal of Molecular Liquids ; Volume 317 , November , 2020 ; Feyzi, P ; Tourang, S ; Tajfar, F ; Sharif University of Technology
Elsevier B. V
2020
Abstract
A reliable model was used to describe the interfacial tension, composition, and density of the liquid-liquid interfaces of water-hydrocarbons. The parachor model was combined with the equality of the chemical potential of components at the interface and the bulk liquid. The fugacity coefficient was used for computing chemical potentials. To compute the fugacity coefficients of the components, various types of equations of state (The Valderrama Patel-Teja, cubic plus association, and the simplified Perturbed-Chain Statistical Association Fluid Theory) were utilized. These models were applied to the temperature and the pressure range of (285.65–423) K and (1–3000) bar, respectively. The...