Balancing the trade-off between prediction accuracy and computational efficiency is essential when selecting the most suitable ML model for a given task. In practice, this choice often depends on the specific requirements and constraints of the application domain. In our study, both LGBM and GATGNN exhibit simultaneous superiority in prediction accuracy and computational efficiency for E f prediction, evidenced by their high R 2 scores, low MAE values (see Table ), and minimal time consumption per data point (see Figure ). However, in the case of E g prediction, while LGBM demonstrates comparable computational efficiency to GATGNN (see Figure ), it shows inferior prediction accuracy compared to GATGNN across three datasets (see Table ). This underperformance is particularly notable in the CMR dataset, where the R 2 of LGBM falls below 0.3.