We first formed a test set using all T2-based structures contained in LE all (a dataset comprising CSP landscapes of T2 and analogues of T2). The remaining structures were then randomly divided into training and validation sets in a 90%:10% ratio to fine-tune MCRT. Subsequently, from the T2 structures, we sequentially separated 100, 200, 300, ..., up to 1,000 structures to further fine-tune the model obtained from the previous step. The remaining structures were used as a test set to assess the robustness of MCRT predictions. To provide a clear performance comparison, we conducted the same experiment using ALIGNN, the most competitive baseline model for lattice energy prediction (Table ). Fig. illustrates our training setup and Fig. presents the MAE, R 2 and top-10 prediction performances for MCRT and ALIGNN. In the zero-shot scenario, MCRT exhibits significantly higher prediction accuracy compared to ALIGNN, indicating that after learning from similar structures, MCRT can generalize more effectively to related but unseen structures. Furthermore, with just an extra step of further fine-tuning on 100 structures, MCRT scores a low prediction MAE (4.34 kJ/mol). By contrast, even after being trained on 10 times more data, ALIGNN still fails to achieve MCRT's extrapolation capacity, with a prediction MAE of 6.40 kJ/mol. This was further echoed by a series of energy-density landscape reconstruction tasks on the different finetuned MCRT models. As shown in Fig. , MCRT accurately reproduces the relative positions of the four experimental structures within the energy-density landscape even with zero-shot learning, whereas ALIGNN fails to effectively capture these relative positions, particularly misplacing T2-δ by conflating it with numerous other structures, as illustrated in Fig. . This further underscores MCRT's practicality for computationally costly scenarios such as crystal structure prediction.