Learning accurate representations is a crucial aspect of machine learning theory that also extends to learning molecular representations. Different types of materials pose different challenges when learning accurate latent representations. For example, in solid-state systems it is essential to capture features such as long-range interactions and periodicity in property prediction tasks . This is especially challenging in organic molecular crystals due to the intermolecular interactions , which are typically weaker than for ionic inorganic materials. Hence, many of the inherently local graph-based deep architectures fail to capture global-driven properties, while traditional ML models that use hand-crafted descriptors have proved more successful in capturing spatial information in some cases . Hand-crafted descriptors such as smooth overlap of atomic positions (SOAP) and atom-centered symmetry functions (ACSFs) have proven effective in predicting properties like lattice energy , while geometric descriptors such as accessible surface area and pore diameters have been used to predict the methane deliverable capacity of molecular crystals as well as other global-driven properties of porous materials . More recently, persistent homology has been shown to successfully encode global molecular geometric features into machinelearned representations . Nevertheless, calculating descriptors such as SOAP can be cumbersome in terms of memory footprint for larger organic systems, whereas geometric descriptors tend to overly compress the geometric information of the crystals, failing to adequately encode the fine detail of the molecular geometry. Another fundamental drawback of deep learning models is the need for re-training and hyperparameter reoptimization for each specific problem and property, adding further time and computational cost. A further challenge is the availability of training data: ideally, we need methods that can be fine-tuned on small scale datasets, because for chemistry problems, data is often scarce and expensive.