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
PACMaN.predict(cif_file="./test/Cu-BTC.cif",model_name="MOF",charge_type="DDEC6",digits=10,atom_type=True,neutral=True)
- cif_file: cif file (without partial atomic charges) [cif path]
- model-name (default: MOF): MOF or COF
- charge-type (default: DDE6): DDEC6, Bader or CM5
- 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
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 using Crystal Graph Convolution Network},
journal={Journal of Chemical Theory and Computation},
author={Zhao, Guobin and Chung, Yongchul},
year={2024},
}
Bugs
If you encounter any problem during using PACMAN, please email sxmzhaogb@gmail.com
.
Group: Molecular Thermodynamics & Advance Processes Laboratory
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
PACMAN-charge-0.1.7.tar.gz
(15.7 kB
view hashes)
Built Distribution
Close
Hashes for PACMAN_charge-0.1.7-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | fd83a35d66cf7aa9060c8ddb6da3a46f77ede5f511ec93e9f6e93e8672352a47 |
|
MD5 | c8a79b4e4d734cee6248b22a8ffc95eb |
|
BLAKE2b-256 | 4ef601a94106ac93c97c1c5eb541b588a7b0b35a16dd61aea912143ac8ab2b39 |