Explicit expressions of the saturation flux density and thermal stability in Fe-based metallic glasses based on Lasso regression

Abstract Fe-based soft magnetic metallic glasses (MGs) have great potential for a wide variety of applications due to their low material cost and excellent soft magnetic properties. At present, a satisfactory combination of the saturation flux density (Bs) and thermal stability (Tx) in the above-mentioned MGs is of great challenge if the conventional trial-and-error method is adopted. In order to explore explicit expressions for predicting Bs and Tx of Fe-based soft magnetic MGs instead of implicit and unexplained machine learning (ML) models, herein, Lasso regression approach was employed and the predictive performance of the developed explicit expressions based on different input descriptors were analyzed. The obtained results show that there exists a highly linear relationship among composition, structure and property in Fe-based MGs. The studied explicit expressions of Bs and Tx exhibit good prediction efficiency with the high R2 scores of 0.952 and 0.968, respectively, which are both superior to the previously reported corresponding R2 values in the literature. This work suggests that Lasso regression possesses a great potential for assessing the quantitative composition-structure-property relationship, and thus might provide some hints to discover new Fe-based soft magnetic MGs.