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Designing novel Multicomponent Metallic Glasses (MMGs) based on empirical parameters such as enthalpy of mixing () and configurational entropy () 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 () 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 proposedto predict MMGs. The ML findings demonstrate that proposedcan be utilised as a salient attribute to predict MMGs with 87.8% cross-validation accuracy. Further, the mean square variation in theof the alloy constituents () 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.

A new approach to design multicomponent metallic glasses using the mendeleev number