Covalent organic frameworks (COFs) are a class of advanced materials that can be precisely engineered for diverse applications, including catalysis, flexible electronics, and sensors. However, COFs synthesised experimentally often exhibit a variety of structural defects and grain boundaries, which affect their properties. Due to their large and complex structure, COFs pose a considerable challenge for traditional ab initio methods. To mitigate this issue, Machine Learning Interatomic Potentials (MLIP) can be used to significantly accelerate property calculations, while retaining near *ab-inito* accuracy. Our team have parameterised a MLIP using MACE architecture and a dataset of non-equilibrium conformations of 2D COFs. We have assessed the transferability of the MACE model computing atomic forces and phonon dispersions of unseen COFs, and compared these results to ReaxFF and reference data by Density Functional Theory using VASP code. Here, we will also discuss the generalisability of our model in predicting thermal transport and elastic properties.