Table reports the mean absolute error (MAE) results for fine-tuning MCRT and its variants, compared against state-of-the-art baseline models. SOAP-based random forest (RF) and kernel ridge regression (KRR) , graph-based CGCNN and ALIGNN , and pre-trained crystal twins (CT) were selected as baseline models due to their universality and competitive performance in predicting materials' properties . For a detailed description of these methods and their featurisations, as well as the MCRT variants used in benchmarking, see Section 4. For lattice energy prediction, three datasets of different sizes were used to validate the model's transferability capabilities under limited data availability scenarios. We note here that LE all includes the CSP landscapes of 10 molecules, all with hydrogen bonding functionality (see Fig. , below), comprising 73,779 structures in total with their associated lattice energies. LE T2 represents the CSP landscape of the T2 molecule with 8,293 structures and energies, while LE T2A corresponds to the CSP landscape of the T2A molecule with 1,367 structures and energies. Both LE T2 and LE T2A are subsets of LE all.