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Machine learning (ML) models have emerged as powerful tools for accelerating materials discovery and design by enabling accurate predictions of properties from compositional and structural data. These capabilities are vital for developing advanced technologies across fields such as energy, electronics, and biomedicine, potentially reducing the time and resources needed for new material exploration and promoting rapid innovation cycles. Recent efforts have focused on employing advanced ML algorithms, including deep learning-based graph neural networks, for property prediction. Additionally, ensemble models have proven to enhance the generalizability and robustness of ML and Deep Learning (DL). However, the use of such ensemble strategies in deep graph networks for material property prediction remains underexplored. Our research provides an in-depth evaluation of ensemble strategies in deep learning-based graph neural network, specifically targeting material property prediction tasks. By testing the Crystal Graph Convolutional Neural Network (CGCNN) and its multitask version, MT-CGCNN, we demonstrated that ensemble techniques, especially prediction averaging, substantially improve precision beyond traditional metrics for key properties like formation energy per atom $\left(\mathrm{\Delta }{E}^f\right)$, band gap $\left(E_g\right)$, density $\left(\rho \right)$, equivalent reaction energy per atom $\left(E_{\mathit{rxn,atom}}\right)$, energy per atom $\left(E_{\mathit{atom}}\right)$and atomic density $\left({\rho }_{\mathit{atom}}\right)$in 33,990 stable inorganic materials. These findings support the broader application of ensemble methods to enhance predictive accuracy in the field. 

Abstract A two-dimensional material – Mg 2 B 4 C 2 , belonging to the family of the conventional superconductor MgB 2 , is theoretically predicted to exhibit superconductivity with critical temperature T c estimated in the 47–48 K range (predicted using the McMillian-Allen-Dynes formula) without any tuning of external parameters such as doping, strain, or substrate-induced effects. The origin of such a high intrinsic T c is ascribed to the presence of strong electron-phonon coupling and large density of states at the Fermi level. This system is obtained after replacing the chemically active boron-boron surface layers in a MgB 2 slab by chemically inactive boron-carbon layers. Hence, the surfaces of this material are inert. Our calculations confirm the stability of 2D Mg 2 B 4 C 2 . We also find that the key features of this material remain essentially unchanged when its thickness is increased by modestly increasing the number of inner MgB 2 layers.