reacnetgenerator

所属分类:人工智能/神经网络/深度学习
开发工具:Python
文件大小:0KB
下载次数:0
上传日期:2024-03-04 08:26:10
上 传 者sh-1993
说明:  用于 React分子动力学模拟的自动 React网络发生器
(an automatic reaction network generator for reactive molecular dynamics simulation)

文件列表:
conda/recipe/
docs/
reacnetgenerator/
tests/
.license-header.txt
.pre-commit-config.yaml
CITATION.cff
Dockerfile
LICENSE
codecov.yml
pyproject.toml
renovate.json
setup.py
tox.ini

# ReacNetGenerator [![DOI:10.1039/C9CP05091D](https://img.shields.io/badge/DOI-10.1039%2FC9CP05091D-blue)](https://doi.org/10.1039/C9CP05091D) [![Citations](https://citations.njzjz.win/10.1039/C9CP05091D)](https://doi.org/10.1039/C9CP05091D) [![Research Group](https://img.shields.io/website-up-down-green-red/https/computchem.cn.svg?label=Research%20Group)](https://computchem.cn) An automatic reaction network generator for reactive molecular dynamics simulation. ReacNetGenerator: an automatic reaction network generator for reactive molecular dynamic simulations, Phys. Chem. Chem. Phys., 2020, 22 (2): 683–691, doi: [10.1039/C9CP05091D](https://dx.doi.org/10.1039/C9CP05091D) jinzhe.zeng@rutgers.edu (Jinzhe Zeng), tzhu@lps.ecnu.edu.cn (Tong Zhu) ## Features - Processing of MD trajectory containing atomic coordinates or bond orders - Hidden Markov Model (HMM) based noise filtering - Isomers identifying accoarding to SMILES - Generation of reaction network for visualization using force-directed algorithm - Parallel computing ## Guide and Tutorial The latest version requires Python 3.7 or later. You can install ReacNetGenerator with `conda`: ```sh conda install reacnetgenerator -c conda-forge reacnetgenerator -h ``` See [the guide](https://reacnetgenerator.njzjz.win/guide/) to learn how to install and use ReacNetGenerattor. We also provide [a series of tutorials](https://reacnetgenerator.njzjz.win/tutorial/) to help you learn ReacNetGenerator. ## Awards * The First Prize in 2019 (the 11th Session) Shanghai Computer Application Competition for College Students * The First Prize in 2019 (the 12th Session) Chinese Computer Design Competition for College Students ## Acknowledge * National Natural Science Foundation of China (Grants No. 91641116) * National Innovation and Entrepreneurship Training Program for Undergraduate (201910269080) * ECNU Multifunctional Platform for Innovation (No. 001)

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