Engineering of Novel Fe-Based Bulk Metallic Glasses Using a Machine Learning-Based Approach

Abstract

A broad range of potential chemical compositions makes difficult design of novel bulk metallic glasses (BMGs) without performing expensive experimentations. To overcome this problem, it is very important to establish predictive models based on artificial intelligence. In this work, a machine learning (ML) approach was proposed for predicting glass formation in numerous alloying compositions and designing novel glassy alloys. The results showed that our ML model accurately predicted the glass formation and critical thickness of MGs. As a case study, the ternary Fe-B-Co system was selected and effects of minor additions of Cr, Nb and Y with different atomic percentages were evaluated. It was found that the minor addition of Nb and Y leads to the significant improvement of glass-forming ability (GFA) in the Fe-B-Co system; however, a shift in the optimized alloying composition was occurred. The experimental results on selective alloying compositions also confirmed the capability of our ML model for designing novel Fe-based BMGs.

A critical review of the machine learning guided design of metallic glasses for superior glass-forming ability

“…While this process of data pre-processing can be vital for ML modeling, particularly for a limited data size, we note that it has often been neglected in previous studies [14,15,18,42] . As noted by Liu et al [24] and Zhou et al [31] , a GFA dataset built from successful experiments can be significantly biased if it only includes the data for good glassforming alloys, thereby potentially compromising the efficiency of either classification or regression ML models.…”

“…To mitigate this problem, one can create a more balanced dataset by data undersampling [24] or oversampling [31] , which is particularly useful for classification ML modeling. In addition, one can perform data transformation such that the distribution of the transformed data becomes closer to a normal distribution than before.…”

“…Therefore, by following the empirical rules [6,45,46] , researchers can translate compositional information into data descriptors in order to enhance the performance of ML modeling. These empirical descriptors could include mean atomic size [20][21][22]47] , atomic size difference [16,21,24,47] , mixing enthalpy [20][21][22]47] , ideal mixing entropy [20][21][22]24,47] , mean electronegativity [16,20,22,47] , electronegativity difference [24,47] and mean valence electron concentration [20,22,24,47] .…”

“…In theory, PCA extracts the eigenvectors from the covariance matrix of the data with descriptors and only keeps those with the largest eigenvalues. In doing so, PCA can retain the most important information by keeping the descriptors with high coefficients of variation while discarding those with low coefficients of variance [24,26] , as exemplified by Figure 4A. In addition, one can also calculate the Pearson's correlation coefficient (PCC) [22,24,26,27] of data descriptors, which measures the linear correlation between any two descriptors, as shown in Figure 4B.…”

“…Given that the design of MGs always has a target property, supervised learning, which has been well developed to seek the correlation between input descriptors and output labels, is the most preferred approach in ML modeling for MG design. To date, researchers have already built different supervised learning-based ML models to address various problems related to MG design, such as the GFA of alloys [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27] , GFA-related characteristic temperatures [13,14,19,20,23,25,28,29] and various other mechanical and magnetic properties [19,22,30] . With the hitherto reported data, one can design data descriptors based on compositional information, empirical parameters or physical theories.…”