A new approach to design multicomponent metallic glasses using mendeleev number

Designing novel Multicomponent Metallic Glasses (MMGs) based on empirical parameters such as enthalpy of mixing (Delta H-mix) and configurational entropy (Delta S-mix) 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 MNp can be utilised as a salient attribute to predict MMGs with 87.8% cross-validation accuracy. Further, the mean square variation in the MN(p )of the alloy constituents (Delta MNp) 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.

Automated summary

This study proposes a modified Mendeleev Number (MNp) element scale based on important elemental properties to predict Multicomponent Metallic Glasses (MMGs) using machine learning. The results show that the proposed MNp is a significant attribute for predicting MMGs with an accuracy of 87.8% in cross-validation. Additionally, the mean square variation in the MNp of the alloy constituents provides a delineated zone for glass forming multicomponent alloys.