Abstract
The development of high-performance materials based on the accurate optimization of microstructure and property is needed to meet the rapid development of energy storage systems and conversion systems. Traditional methods of computation, including density functional theory (DFT) and molecular dynamics (MD), give essential accuracy and can be computationally demanding, and scale-limited when large-scale exploration of materials is required. To overcome these limitations, this research paper suggests a Quantum-Informed Predictive Deep Graph Neural Network (QD-GNN) model that combines quantum-mechanical simulated capabilities with graph-based deep learning to predict and optimize microstructural performance features, including conductivity, mechanical stability, and thermal resiliency. The model uses DFT-based descriptors and graph-based atomic connectivity representations to learn complex structure-property correlations which allows one to predict properties rapidly without the use of expensive repeated highcost simulation. Also a reinforcement-based optimization tool repeatedly optimizes microstructural configurations to yield desired thresholds in properties. Significant improvements in currently state of art GNN, CGCNN, MEGNet, and SchNet frameworks, in terms of smaller prediction errors, faster screening speed, and better generalization have been verified experimentally through benchmark energy-material datasets. The proposed QD-GNN is a scalable route to AI-intensified discovery of next-generation battery, fuel-cell, and solid-state electrolyte materials.