Predicting shear modulus property using materials informatics.

In this study, a dataset comprising 2574 compositions was extracted from a Materials database. After cleansing the data, the focus was on predicting the relationship between composition and the shear modulus property. This was accomplished by employing the Composition Based Feature Vector (CBFV) technique, using appropriate Classical Machine Learning Algorithms. Additionally, a Deep Neural Network was also employed for further prediction analysis. [ABSTRACT FROM AUTHOR]