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 finetuning 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.