The second idea is as follows. We assert that ab initio charges are required for HTCS of MOFs for carbon capture. Unfortunately, it is still computationally challenging to employ these approaches for large databases of materials. Therefore, we would like to explore whether ML-based models such as the message passing neural network (MPNN) model and partial atomic charges in metal-organic frameworks (PACMOF) model can be used as a scalable but accurate alternative to the DDEC method.