Probabilistic Assessment of Glass Forming Ability Rules for Metallic Glasses Aided by Automated Analysis of Phase Diagrams

Abstract

The use of machine learning techniques to expedite the discovery and development of new materials is an essential step towards the acceleration of a new generation of domain-specific highly functional material systems. In this paper, we use the test case of bulk metallic glasses to highlight the key issues in the field of high throughput predictions and propose a new probabilistic analysis of rules for glass forming ability using rough set theory. This approach has been applied to a broad range of binary alloy compositions in order to predict new metallic glass compositions. Our data driven approach takes into account not only a broad variety of thermodynamic, structural and kinetic based criteria, but also incorporates qualitative and descriptive attributes associated with eutectic points in phase diagrams. For the latter, we demonstrate the use of automated machine learning methods that go far beyond text recognition approaches by also being able to interpret phase diagrams. When combined with structural descriptors, this approach provides the foundations to develop a hierarchical probabilistic predication tool that can rank the feasibility of glass formation.

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

“…As shown in Table 1, the current design of data descriptors is mostly based on alloy compositions and empirical rules. Although the compositional descriptors can be readily calculated once an alloy is known [13,17,29] , they lack physical significance and require other types of complementary descriptors [8,13,26] . 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.…”

“…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...…”

“…These numeric metrics provide the results of the immediate evaluation of the predictability of the ML models after data training. Based on the current literature, it appears that the ML models, particularly the GFA classifiers [8,20,[22][23][24]27,31,33] , exhibit a very good performance according to the results of numerical evaluation.…”

“…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.…”