accurate dft-quality partial atomic charges for MOFs and COFs

PACMAN is a fast and easy application that can predict density-derived electrostatic and chemical (DDEC06), Charge Model 5 (CM5), Bader, and REPEAT partial atomic charges based on a crystal graph convolution neural network (CGCNN) model. 

• Github repository

• APP

Citation: G. Zhao, Y.G. Chung "PACMAN: A Robust Partial Atomic Charge Predicter for Nanoporous Materials Based on Crystal Graph Convolution Networks"  Journal of Chemical Theory and Computation, 20, 12, 5352 - 5367, (2024)