d. e. f. Here, we examined the predictive capabilities of both CML and GNN based models trained with varying data volumes to explore the impact of dataset size on model performance. Specifically, we sampled portions of data from each dataset, adhering to the same training, validation, and test set split as detailed in Section 3.2. To mitigate potential variations in prediction results stemming from differences in data distribution, we conducted four trials of model training using various random seeds for data sampling. Subsequently, we computed the average R 2 and MAE values on the test set. This approach ensures a more reliable and consistent evaluation of model performance across diverse dataset sizes, as supported by previous studies. Figure shows MAE and R 2 values of eight models for predicting E f across three datasets of varying sizes. For each dataset, model training commenced with 200 data points and incrementally expanded the data size until the entire dataset was incorporated into the model. In general, a continuous decrease in MAE and increase in R 2 were observed across all ML models as the data size increased, irrespective of the dataset utilized. Notably, the majority of ML models demonstrate a rapid performance enhancement as the training data size increases from 200 to 1000, except that CML models in the OQMD dataset exhibit deteriorating performance with larger training sets (see Figure ). Beyond 1000 data points, the performance of all the models exhibits negligible improvement, with converged R 2 and MAE values.