Brittle and ductile characteristics of intermetallic compounds in magnesium alloys: A large-scale screening guided by machine learning

In the present work, we have employed machine learning (ML) techniques to evaluate ductile-brittle (DB) behaviors in intermetallic compounds (IMCs) which can form magnesium (Mg) alloys. This procedure was mainly conducted by a proxy-based method, where the ratio of shear (G)/bulk (B) moduli was used as a proxy to identify whether the compound is ductile or brittle. Starting from compounds information (composition and crystal structure) and their moduli, as found in open databases (AFLOW), ML-based models were built, and those models were used to predict the moduli in other compounds, and accordingly, to foresee the ductile-brittle behaviors of these new compounds. The results reached in the present work showed that the built models can effectively catch the elastic moduli of new compounds. This was confirmed through moduli calculations done by density functional theory (DFT) on some compounds, where the DFT calculations were consistent with the ML prediction. A further confirmation on the reliability of the built ML models was considered through relating between the DB behavior in MgBe13 and MgPd2, as evaluated by the ML-predicted moduli, and the nature of chemical bonding in these two compounds, which in turn, was investigated by the charge density distribution (CDD) and electron localization function (ELF) obtained by DFT methodology. The ML-evaluated DB behaviors of the two compounds was also consistent with the DFT calculations of CDD and ELF. These findings and confirmations gave legitimacy to the built model to be employed in further prediction processes. Indeed, as examples, the DB characteristics were investigated in IMCs that might from in three Mg alloy series, involving AZ, ZX and WE

Interpretable Machine Learning Analysis of Stress Concentration in Magnesium: An Insight beyond the Black Box of Predictive Modeling

In the present work, machine learning (ML) was employed to build a model, and through it, the microstructural features (parameters) affecting the stress concentration (SC) during plastic deformation of magnesium (Mg)-based materials are determined. As a descriptor for the SC, the kernel average misorientation (KAM) was used, and starting from the microstructural features of pure Mg and AZ31 Mg alloy, as recorded using electron backscattered diffraction (EBSD), the ML model was trained and constructed using various types of ML algorithms, including Logistic Regression (LR), Decision Trees (DT), Random Forest (RF), Naive Bayes Classifier (NBC), K-Nearest Neighbor (KNN), Multilayer Perceptron (MLP), and Extremely Randomized Trees (ERT). The results show that the accuracy of the ERT-based model was higher compared to other models, and accordingly, the nine most-important features in the ERT-based model, those with a Gini impurity higher than 0.025, were extracted. The feature importance showed that the grain size is the most effective microstructural parameter for controlling the SC in Mg-based materials, and according to the relative Accumulated Local Effects (ALE) plot, calculated to show the relationship between KAM and grain size, it was found that SC occurs with a lower probability in the fine range of grain size. All findings from the ML-based model built in the present work were experimentally confirmed through EBSD observations

Interpretable Machine Learning Analysis of Stress Concentration in Magnesium: An Insight beyond the Black Box of Predictive Modeling

In the present work, machine learning (ML) was employed to build a model, and through it, the microstructural features (parameters) affecting the stress concentration (SC) during plastic deformation of magnesium (Mg)-based materials are determined. As a descriptor for the SC, the kernel average misorientation (KAM) was used, and starting from the microstructural features of pure Mg and AZ31 Mg alloy, as recorded using electron backscattered diffraction (EBSD), the ML model was trained and constructed using various types of ML algorithms, including Logistic Regression (LR), Decision Trees (DT), Random Forest (RF), Naive Bayes Classifier (NBC), K-Nearest Neighbor (KNN), Multilayer Perceptron (MLP), and Extremely Randomized Trees (ERT). The results show that the accuracy of the ERT-based model was higher compared to other models, and accordingly, the nine most-important features in the ERT-based model, those with a Gini impurity higher than 0.025, were extracted. The feature importance showed that the grain size is the most effective microstructural parameter for controlling the SC in Mg-based materials, and according to the relative Accumulated Local Effects (ALE) plot, calculated to show the relationship between KAM and grain size, it was found that SC occurs with a lower probability in the fine range of grain size. All findings from the ML-based model built in the present work were experimentally confirmed through EBSD observations

A Comparative Study of Strain Rate Constitutive and Machine Learning Models for Flow Behavior of AZ31-0.5 Ca Mg Alloy during Hot Deformation

In this study, isothermal compression tests of highly ductile AZ31-0.5Ca Mg alloys were conducted at different strain rates (0.001–0.1 s−1) and temperatures (423–523 K) along with extruded direction. The flow stress characteristics were evaluated at elevated temperatures. In addition, a strain-dependent constitutive model based on the Arrhenius equation and machine learning (ML) were constructed to evaluate the stress–strain flow behavior. To build the ML model, experimental data containing temperature, strain, and strain rate were used to train various ML algorithms. The results show that under lower temperatures and higher strain rates, the curves exhibited strain hardening, which is due to the higher activation energy, while when increasing the temperature at a fixed strain rate, the strain hardening decreased and curves were divided into two regimes. In the first regime, a slight increase in strain hardening occurred, while in the second regime, dynamic recrystallization and dynamic recovery controlled the deformation mechanism. Our ML results demonstrate that the ML model outperformed the strain-dependent constitutive model

A Comparative Study of Strain Rate Constitutive and Machine Learning Models for Flow Behavior of AZ31-0.5 Ca Mg Alloy during Hot Deformation

In this study, isothermal compression tests of highly ductile AZ31-0.5Ca Mg alloys were conducted at different strain rates (0.001–0.1 s−1) and temperatures (423–523 K) along with extruded direction. The flow stress characteristics were evaluated at elevated temperatures. In addition, a strain-dependent constitutive model based on the Arrhenius equation and machine learning (ML) were constructed to evaluate the stress–strain flow behavior. To build the ML model, experimental data containing temperature, strain, and strain rate were used to train various ML algorithms. The results show that under lower temperatures and higher strain rates, the curves exhibited strain hardening, which is due to the higher activation energy, while when increasing the temperature at a fixed strain rate, the strain hardening decreased and curves were divided into two regimes. In the first regime, a slight increase in strain hardening occurred, while in the second regime, dynamic recrystallization and dynamic recovery controlled the deformation mechanism. Our ML results demonstrate that the ML model outperformed the strain-dependent constitutive model

Predictive modeling of critical temperatures in magnesium compounds using transfer learning

This study presents a transfer learning approach for discovering potential Mg-based superconductors utilizing a comprehensive target dataset. Initially, a large source dataset (Bandgap dataset) comprising approximately ∼75k compounds is utilized for pretraining, followed by fine-tuning with a smaller Critical Temperature (Tc) dataset containing ∼300 compounds. Comparatively, there is a significant improvement in the performance of the transfer learning model over the traditional deep learning (DL) model in predicting Tc. Subsequently, the transfer learning model is applied to predict the properties of approximately 150k compounds. Predictions are validated computationally using density functional theory (DFT) calculations based on lattice dynamics-related theory. Moreover, to demonstrate the extended predictive capability of the transfer learning model for new materials, a pool of virtual compounds derived from prototype crystal structures from the Materials Project (MP) database is generated. Tc predictions are obtained for ∼3600 virtual compounds, which underwent screening for electroneutrality and thermodynamic stability. An Extra Trees-based model is trained to utilize Ehull values to obtain thermodynamically stable materials, employing a dataset containing Ehull values for approximately 150k materials for training. Materials with Ehull values exceeding 5 meV/atom were filtered out, resulting in a refined list of potential Mg-based superconductors. This study showcases the effectiveness of transfer learning in predicting superconducting properties and highlights its potential for accelerating the discovery of Mg-based materials in the field of superconductivity