Partial Atomic Charges for Porous Materials based on Graph Convolutional Neural Network (PACMAN)
Project description
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)
- 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{,
title={PACMAN: A Robust Partial Atomic Charge Predicter for Nanoporous Materials based on Crystal Graph Convolution Network},
author={Zhao, Guobin and Chung, Yongchul},
journal={Journal of Chemical Theory and Computation},
year={2024},
DOI={10.1021/acs.jctc.4c00434}
}
Bugs
If you encounter any problem during using PACMAN, please email sxmzhaogb@gmail.com
.
Group: Molecular Thermodynamics & Advance Processes Laboratory
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