The proposed methodology addresses a critical challenge in materials science by attempting to bridge the gap between complex atomic structure and thermodynamic properties using machine learning. The novelty lies in the explicit, multi-scale integration of crystal structure data—combining fine-grained local geometry (GNN) with coarse-grained compositional data (fractional NN). This approach attempts to capture the synergistic effects of local bonding environments and overall composition that gove...