Correlative study between elastic modulus and glass formation in ZrCuAl(X) amorphous system using a machine learning approach

Latest Advances in Manufacturing and Machine Learning of Bulk Metallic Glasses

“…There are many algorithms to achieve such a goal, with most researchers utilizing min-max rescaling as data standardization. [324,347,350,351] As a second step, high-quality data descriptors must be selected. At present, there are four main conventional points of view for devising a descriptor for AIs in amorphous alloys.…”

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

“…Although one may easily retrieve GFA data from prior works, data pre-screening and/or data transformation in order to rule out low-fidelity data usually needs to be performed. 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.…”

“…Once the high-fidelity data have been properly represented, one can develop ML models based on the available ML algorithms. Figure 5A shows the variety of ML algorithms that have been applied in the design of MGs, which include support vector machines (SVMs) [8,18,19,21,26,27,31] , artificial neural networks (ANNs) [13][14][15]18,20,21,24,28,29,31] , k-nearest neighbors [21,27] , neighborhood components analysis [34] , decision trees [9,11,17,21,26,31] , random forests (RFs) [10,12,16,[21][22][23][25][26][27]31,33,42] , fusion algorithms [27] , linear regression [18,26] , Gaussian process regress [21,31] , least absolute shrinkage and selection operator [12] , ridge regression [12] and symbolic regression. These algorithms can gage the effect of data descriptors by a parameter generated by the des...…”

“…Until now, the most commonly used validation methods have been hold-out validation [24] and k-fold crossvalidation [22] . In addition, one can also use the root mean square error [20,24,31] , the coefficient of determination (R 2 ) [10,12,16,17,31,42] and the correlation coefficient (R) [14,15,18,20,22,23,25,28] to validate the predictability of a regression ML model. As we previously discussed, the quality of the available GFA data is adversely affected by experimental measurements (e.g., round-up error [31,51] ) and the data distribution (e.g., high skewness [16,17,31] ).…”

“…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.…”