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.

翻译：机器学习模型已成为加速材料发现与设计的有力工具，能够基于成分与结构数据实现性能的精确预测。这些能力对于能源、电子学与生物医学等领域的先进技术发展至关重要，可显著缩短新材料探索所需的时间与资源，并推动快速创新循环。近期研究聚焦于采用先进的机器学习算法，包括基于深度学习的图神经网络，进行性能预测。此外，集成模型已被证明能够提升机器学习与深度学习的泛化能力与鲁棒性。然而，此类集成策略在用于材料性能预测的深度图网络中的应用仍待深入探索。本研究对基于深度学习的图神经网络中的集成策略进行了深入评估，特别针对材料性能预测任务。通过测试晶体图卷积神经网络及其多任务版本MT-CGCNN，我们证明了集成技术（尤其是预测平均法）能显著提升对33,990种稳定无机材料的关键性能——如每个原子的形成能($\Delta E^{f}$)、带隙($E_{g}$)与密度($\rho$)——的预测精度，其效果超越传统评估指标。这些发现支持在材料预测领域更广泛地应用集成方法以提升预测准确性。