Structure divide-and-conquer: dual graph representation for accurate ionic transport barrier prediction of inorganic compounds

Despite the effectiveness of most graph-based representation methods in capturing the crystal geometry characteristics, they fall short in intuitively describing phenomena such as ionic transport behavior which are often determined by the atom-unoccupied regions in the mobile sublattice (namely interstitial network). Here, we develop a Structure Divide-and-Conquer Graph Representation method based on graph neural network (SDCGNN dk ), for unveiling structure-activity relationships of transport barriers by incorporating Domain Knowledge (e.g., site energy information, thresholds for ion accessibility, etc.), where crystal geometry and interstitial network topology are combined to construct a dual-structure crystal graph. For driving the proposed model, we construct a graph-based dataset for the prediction of activation energy ( \({E}_{a}\) ), i.e., the energy barrier hindering ionic transport, covering over 18,000 ionic compounds from the Inorganic Crystal Structure Database (ICSD), including Li + , Na + , K + , Ag + , Cu (2,3)+ , Mg 2+ , Zn 2+ , Ca 2+ , Al 3+ , F − , and O 2− . SDCGNN dk achieves high prediction performance of \({E}_{{\rm{a}}}\) with R 2 of 91.30%, outperforming conventional GNNs by more than 20% on average and offering insights into structure-activity relationships by quantifying the contributions of crystal geometry and interstitial network characteristics to transport barriers. This work provides an accurate graph representation and GNN framework, demonstrating potential for extension to predicting other properties relevant to interstitial network of inorganic compounds.