The key to implementing successful machine learning model are descriptors, relevant mathematical representations of the structure and properties of materials. Fragment-based descriptors have demonstrated superior performance for ML models of molecules and crystals. In this work we adopt the method of Isayev et al., the Property-Labelled Materials Fragments (PLMF), modified for 2D materials, composed of 1529 descriptors. In the PLMF approach a crystal structure is represented as a graph, with vertices labelled according to the reference properties of the atoms they represent and nodes are connecting topological neighbours using Voronoi tessellation. The adjacency matrix of this graph determines the global topology for a given system, including interatomic bonds and contacts within a crystal. The final descriptor vector used to train the Machine Learning (ML) model is obtained by partitioning a full graph into smaller subgraphs, which we call fragments by the analogy with fragment-based descriptors in cheminformatics. Every fragment starts from a node (an atom and its properties) and captures a path in the graph through a collection of bonded atoms. Given the monolayer descriptors, the next critical problem is how to represent each bilayer using the monolayer data. As the interaction between the monolayers in a bilayer is dispersive, it does not affect the structure of either of the constituting monolayers. Therefore, it is possible to use the monolayer descriptors to describe the monolayers in the bilayer. The next issue is how to construct the bilayer descriptor vector. An intuitive choice is to create a larger descriptor vector composed of PLMF vector descriptors for the two monolayers. The problem in this approach is that it is sensitive to the swapping of the bilayers; that is, the descriptor vector for the bilayer A-B, made from monolayers A and B, will be a different vector from that of the bilayer B-A, even though the two bilayers are physically identical. We addressed this problem by generating the descriptor vectors in different two ways.