Machine learning force fields (MLFFs) have come to the forefront to tackle this challenge (Figure and (b)), combining the low computational cost of classical force fields with the high accuracy of ab initio methods. While various materials including alloys, metal oxides, and perovskites have been widely studied, the applications of MLFF for MOFs have still been limited, especially for heterogeneous systems that contain the MOF material itself and the guest molecules. Although a few studies have explored the development of MLFFs for the combined system of MOF with gas molecules such as CO2 or H2, the transferability across different MOF systems remains significantly limited. This limitation is primarily due to the lack of comprehensive databases that contain density functional theory (DFT) simulation data involving the combined system of MOFs with gas molecules due to their expensive computational cost.