Molecular simulation-derived features for machine learning predictions of metal glass forming ability

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

“…In order to reduce the dimension of data descriptors, one usually keeps those with a high PCC magnitude (e.g., |PCC| > 0.8) and removes the rest. In addition to PCA and PCC, we note that the following descriptor selection algorithms, such as the sequential backward selector [11,48] , the exhaustive feature selector [11,48] and the ReliefF algorithm [22,49] , are also available, which have already proved effective in improving the performance of ML modeling [11,22,24,26,27,41] . With respect to data labeling, we note that data labels are usually taken directly from the targeted properties, such as GFA, elastic modulus and GFA-related characteristic temperatures, for regression ML modeling, as shown in Table 1.…”

“…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...…”

“…According to the literature [21,23,24,31] , the overall accuracy of a GFA classifier is ~80%-90%. Apart from that, the model precision and recall of a specific class [11,24] are another two metrics for the performance of a GFA classifier, which are characterized by the confusion matrix. Figure 6A shows the confusion matrix of a well-trained GFA classifier, which has the entry of 81.6% for the positive predictive value (PPV) for the precise prediction of MG, 88.8% for the true positive rate (TPR) for the recall of MG, 82.9% for the negative predictive value (NPV) for the precise prediction of non-MG and 73.1% for the true negative rate (TNR) for the recall of the non-MG class [24] .…”

“…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.…”

Quantum Machine Learning Applications in the Biomedical Domain: A Systematic Review

“…Researchers have employed different QML algorithms in the healthcare domain in recent years. Including the very famous Quantum Support Vector Machine [12], Quantum inspired ML [13], Variational Quantum Classifier [14], Quantum Neural Network [15], Quantum Random Access Coding [16], Quantum Convolutional Neural Network [17], Quantum Deep Neural Network [18], Quantum Boltzmann Machine [19], Autonomous Perceptron Model [20], Hybrid Quantum Feature Selection Algorithm [21], and Quantum Nearest Mean Classifier [22] on publicly available UCI ML healthcare datasets [23], and some of them employed on private healthcare datasets. In this manuscript, we deep dive into analyzing the applications of QML in the healthcare sector, especially in biomedical domain.…”