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• W4295162433 abstract "Designing novel Multicomponent Metallic Glasses (MMGs) based on empirical parameters such as enthalpy of mixing (ΔHmix) and configurational entropy (ΔSmix) is a time-consuming exercise that requires various assumptions, limiting the capability to predict new MMG compositions. The current study involves constructing a modified Mendeleev Number (MNP) element scale based on many important elemental properties that impact the glass forming phenomena. Machine learning (ML) was used to assess the competence of the proposed MNP to predict MMGs. The ML findings demonstrate that proposed MNPcan be utilised as a salient attribute to predict MMGs with 87.8% cross-validation accuracy. Further, the mean square variation in the MNP of the alloy constituents (ΔMNP) provides a delineated zone of glass forming multicomponent alloys. In summary, the research work presents a novel phenomenological coordinate system that can effectively predict new MMGs while avoiding the limitations of empirical parameters based design strategies." @default.
• W4295162433 created "2022-09-11" @default.
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• W4295162433 date "2022-09-11" @default.
• W4295162433 modified "2023-10-18" @default.
• W4295162433 title "A new approach to design multicomponent metallic glasses using the mendeleev number" @default.
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• W4295162433 doi "https://doi.org/10.1080/14786435.2022.2121868" @default.
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