The overall framework of MCRT is illustrated in Fig. . It comprises a transformer encoder module that is used to build a pre-trained model that then acts as foundation for fine-tuning on a range of downstream prediction tasks. The pre-trained model was built with universality in mind, and designed to distill critical features of molecular crystals without the need for labeled data and, subsequently, to extrapolate desirable physical properties across various applications after fine-tuning. This transformer is therefore designed as a multi-modal architecture that processes two distinct input modalities, which encode both local and global information: atom-based graph embeddings and persistence image embeddings.