Data can be the limiting resource both in practical synthetic chemistry and in computational materials studies due to the high costs of synthetic or computational methods. Here we explored our model's extrapolation capabilities on extremely small datasets. Specifically, we tested MCRT's predictive performance in zero-and few-shot learning scenarios for predicting lattice energies of T2 structures, using datasets containing analogues of T2 to assess its generalization capability within a related molecular family.