Reaction Network Generator
Project description
ReacNetGenerator
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
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
Installation
You can install Anaconda or Miniconda to obtain conda, and install ReacNetGenerator easily with conda:
conda install reacnetgenerator -c conda-forge
See the build guide if you want to build ReacNetGenerator by yourself.
Usage
Command line
ReacNetGenerator can process any kind of trajectory files containing atomic coordinates, e.g. a LAMMPS dump file prepared by running “dump 1 all custom 100 dump.reaxc id type x y z” in LAMMPS:
reacnetgenerator --dump -i dump.reaxc -a C H O
where C, H, and O are atomic names in the input file. Analysis report will be generated automatically.
Also, ReacNetGenerator can process files containing bond information, e.g. LAMMPS bond file:
reacnetgenerator -i bonds.reaxc -a C H O
You can running the following script for help:
reacnetgenerator -h
GUI version
You can open a GUI version for ReacNetGenerator by typing:
reacnetgeneratorgui
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)
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
Built Distributions
Hashes for reacnetgenerator-1.4.124-cp38-cp38-manylinux2010_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 6c82d4bb7f2f2fdb0629d06657cd7cd7523633eaa62d422b7a04f711c120a99a |
|
MD5 | 0f47c228afe0465148c7533e2b8ab093 |
|
BLAKE2b-256 | 79421669f5810721a74d38706d366153405aefc55981557e2a0a587b73a7164e |
Hashes for reacnetgenerator-1.4.124-cp38-cp38-manylinux1_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | c51ec5435fb8b0b8d76f2f3497d4d212b4566c1bd00ff30ae4fd3424f48f1404 |
|
MD5 | 308ea6b587ecc43d48c9f33a246c872a |
|
BLAKE2b-256 | 5c0d45b12ff7d5bfa271bf7484a222a9560e6cc075f39f513607812c3ce62284 |
Hashes for reacnetgenerator-1.4.124-cp37-cp37m-manylinux2010_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 97d8b784df185b2bcf33023a112840204f3ab5eab3100d2964e5864cd16658bb |
|
MD5 | f2d563377f79bd1cfd869c956bde0f0a |
|
BLAKE2b-256 | 3782eda0aec89b0a474f048d49332d8313a8e90dd92c37a349b4d2d422148d6b |
Hashes for reacnetgenerator-1.4.124-cp37-cp37m-manylinux1_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | e4764d544c5edca036364dd85c607b5876852b55da2523cd1d4f90d79c77ec80 |
|
MD5 | 312a50dd71bfc8bbaf41ea5256d2cc70 |
|
BLAKE2b-256 | 9533b7dcd896075c8fd1701e9b0626beb469d443da35c7b59c9e0d31059a1b59 |
Hashes for reacnetgenerator-1.4.124-cp36-cp36m-manylinux2010_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 03f0d5d7b2912f5fd0a9b95e09c0622fad0d327ebc2279d7101106402b23e7c3 |
|
MD5 | ea4318d5627e566cf27bebfaf11d98f9 |
|
BLAKE2b-256 | a3766299529c93cca1493ceb70c7bc1f1c582b7e57d9a716bb581309ef865c5f |
Hashes for reacnetgenerator-1.4.124-cp36-cp36m-manylinux1_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | a93cc7f49fd2ecf0d2fa664d9f41008f132198ba0d0bfc37594392a573c05198 |
|
MD5 | a5076c3fd55861632b48c8f91004cfe6 |
|
BLAKE2b-256 | 9a4407eb2b9ac93bd420f63a31202923fbcd994a558a9597bb90ef0f87209503 |