Partial Atomic Charges for Porous Materials based on Graph Convolutional Neural Networks (PACMAN)
Partial Atomic Charges for Porous Materials based on Graph Convolutional Neural Networks (PACMAN)
PACMAN charge is a fast and easy Python program that can calculate high-fidelity density-derived electrostatic and chemical (DDEC), Charge Model 5 (CM5), Bader, and REPEAT partial atomic charges based on a machine learning model trained on the QMOF dataset.
Current model supports MOFs and COFs.
Citation: G. Zhao, Y.G. Chung "PACMAN: A Robust Partial Atomic Charge Predicter for Nanoporous Materials based on Crystal Graph Convolution Network" Journal of Chemical Theory and Computation, (2024)