Predicting the glass formation of metallic glasses using machine learning approaches

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

“…For instance, the good data or typical MG compositions can be labeled as "1", while the bad data or non-MGs can be labeled as "0" according to Refs. [8,[20][21][22][23][24][25][26][27]33] . Reprinted from Ref.…”

“…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] ).…”

“…As seen in Figure 7A and B, it is clear that the predictions of the RQGPR model fit well with the experiments, while those of the LMANN model are clearly off from the experimental data [Figure 7B], which simply contradicts the results of numerical validation. Unfortunately, we note that only a few ML models were validated by experiments [9,19,20,[23][24][25][26]28,29,42] , so far and it is still not a mandate to carry out experimental validation on ML modeling. One possible reason for the disparity between theory and experiment may be due to the relative scarcity of high-fidelity experimental GFA data, which amount to only several hundred should the data of glassy ribbons be excluded.…”

“…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.…”