As displayed in Figures 6b, atom features are predominant in predicting E f with SHAP% larger than 80% as compared to bond features in both MP and OQMD datasets, whereas in the CMR dataset, the contribution of atom features reduces to around 60%. These variations in feature importance across datasets can be attributed to the distribution similarities in E f values between MP and OQMD datasets (see Figure ), while the CMR dataset was specifically curated to examine diverse A, B, X combinations within identical cubic perovskite structures. This observation highlights the profound impact of data diversity on determining feature importance. However, even though specific feature importance is not consistently selected when training ML models on different datasets, we can still identify trends that offer valuable insights. Firstly, as