For DFT to accurately predict these two quantities, it needs to be corrected by incorporating a vdW correlation potential to the DFT correlation potential. To this end, various forms of the vdW correlation have been proposed and applied to 2D materials, such as the Tkatchenko-Scheffler (TS) method and the SCAN+rVV10 method. , many of which displayed impressive accuracy. DFT databases, such as AFLOW and MaterialsProject, contain structures calculated without consideration of the vdW interaction potential. For example, there are a number of calculations published on the online portals of these databases that have incorrect relaxed c lattice parameter, e.g. MoS 2 -WS 2 in which c = 22.37 Å, an extremely large value compared to the lattice parameter in other hybrid bilayers such as graphene|boron nitride, for which c = 3.3 Å. This error is a result of the use of non-dispersive correlation potentials. Therefore, an essential prerequisite is the construction of a data set of hybrid 2D vdW materials using a dispersion-supported DFT method. However, a critical problem in the application of DFT to hybrid vdW structures is creating lattice-matching interfaces between noncommensurate 2D materials. Using DFT to study hybrid 2D materials requires the supercell describing the interface between two materials (whether parallel or perpendicular to the plane of the 2D materials) to have commensurate supercells for the two materials. One commonly adopted solution to this problem is searching for supercells that minimise the strain in each of the incommensurate monolayers. This is a non-trivial problem that often requires strains of a few percent to keep the size of the supercell reasonable. The use of ML largely bypasses this problem since DFT calculations are only performed on the isolated individual layers and a small subset of bilayers to generate the training and test sets for the ML models.