PACMAN-charge 1.1.2

Description:

PACMANcharge 1.1.2

PACMAN


A Partial Atomic Charge Predicter for Porous Materials based on Graph Convolutional Neural Network (PACMAN)

Usage
from PACMANCharge import pmcharge
pmcharge.predict(cif_file="./test/Cu-BTC.cif",charge_type="DDEC6",digits=6,atom_type=False,neutral=False)
pmcharge.Energy(cif_file="./test/Cu-BTC.cif")


cif_file: cif file (without partial atomic charges) [cif path]
charge-type (default: DDE6): DDEC6, Bader, CM5 or REPEAT
digits (default: 6): number of decimal places to print for partial atomic charges. ML models were trained on a 6-digit dataset.
atom-type (default: True): keep the same partial atomic charge for the same atom types (based on the similarity of partial atomic charges up to 2 decimal places).
neutral (default: True): keep the net charge is zero. We use "mean" method to neuralize the system where the excess charges are equally distributed across all atoms.

Website & Zenodo
PACMAN-APPlink
github repositorylink
DOWNLOAD full code and datasetlink But we will not update new vesion in Zenodo.
Reference
If you use PACMAN Charge, please cite this paper:
@article{doi:10.1021/acs.jctc.4c00434,
author = {Zhao, Guobin and Chung, Yongchul G.},
title = {PACMAN: A Robust Partial Atomic Charge Predicter for Nanoporous Materials Based on Crystal Graph Convolution Networks},
journal = {Journal of Chemical Theory and Computation},
volume = {20},
number = {12},
pages = {5368-5380},
year = {2024},
doi = {10.1021/acs.jctc.4c00434},
note ={PMID: 38822793},
URL = {
https://doi.org/10.1021/acs.jctc.4c00434
},
eprint = {
https://doi.org/10.1021/acs.jctc.4c00434
}
}

Bugs
If you encounter any problem during using PACMAN, please email [email protected].
Group: Molecular Thermodynamics & Advance Processes Laboratory