Feature importance analysis is an inherent capability of transformer models that helps us here to better understand the relationship between molecular crystal structures and their properties. Cumulative attention scores of the [CLS] token were calculated to measure the model's assigned attention to input features according to their importance. An attention rollout strategy was employed by recursively multiplying attention weights across layers, providing more focused patterns for interpreting which input tokens contribute the most to the model's output. Higher attention scores indicate greater importance for the model's prediction. Fig. provides an intuitive visualisation of the explainability scores for an experimentally synthesized structure, T2-γ , for methane capacity (left column) and lattice energy (right column), illustrating explainable feature importance for both modalities.