By incorporating data from abundant source materials such as MOFs, the accuracy of the model predictions can be improved through exploiting the potential synergistic effect between the two material classes. To the best of our knowledge, this type of cross-material transfer learning has yet to be explored in any other materials. Indeed, one can envision that such an approach could enhance the accuracy of machine learning model predictions in overcoming data scarcity challenges through the potential synergistic effect between materials from distinct classes.