  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 network, for property prediction. Additionally, ensemble models
have proven to enhance the generalizability and robustness of ML and 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 ($\Delta
E^{f}$), band gap ($E_{g}$) and density ($\rho$) in 33,990 stable inorganic
materials. These findings support the broader application of ensemble methods
to enhance predictive accuracy in the field.
