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 (T_c) 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 T_c. 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. T_c predictions are obtained for ~3600 virtual compounds, which underwent screening for electroneutrality and thermodynamic stability. An Extra Trees-based model is trained to utilize E_hull values to obtain thermodynamically stable materials, employing a dataset containing E_hull values for approximately 150k materials for training. Materials with E_hull 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.

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.

In this work, a machine learning (ML) model was created to predict intrinsic hardness of various compounds using their crystal chemistry. For this purpose, an initial dataset, containing the hardness values of 270 compounds and counterpart applied loads, was employed in the learning process. Based on various features generated using crystal information, an ML model, with a high accuracy (R² = 0.942), was built using extreme gradient boosting (XGB) algorithm. Experimental validations conducted by hardness measurements of various compounds, including MSi₂ (M = Nb, Ce, V, and Ta), Al₂O₃, and FeB₄, showed that the XGB model was able to reproduce load-dependent hardness behaviors of these compounds. In addition, this model was also used to predict the behavior based on prototype crystal structures that are randomly substituted with elements.