ReacNetGenerator: An automatic reaction network generator for reactive molecular dynamics simulation.

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

Guide and Tutorial

The latest version requires Python 3.7 or later. You can install ReacNetGenerator with conda:

conda install reacnetgenerator -c conda-forge
reacnetgenerator -h

See the guide to learn how to install and use ReacNetGenerattor. We also provide a series of tutorials 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)

Project details

Release history Release notifications | RSS feed

This version

1.6.12

Jan 6, 2024

1.6.11

Dec 7, 2023

1.6.10

Oct 25, 2023

1.6.8

Nov 13, 2022

1.6.7

Nov 5, 2022

1.6.5

Oct 20, 2022

1.6.4

Sep 29, 2022

1.6.3

Aug 29, 2022

1.6.2

Aug 29, 2022

1.6.1

Aug 10, 2022

1.5.2

Apr 30, 2022

1.5.0

Apr 23, 2022

1.4.166

Apr 16, 2022

1.4.165

Sep 14, 2021

1.4.164

Apr 2, 2021

1.4.163

Mar 29, 2021

1.4.162

Mar 25, 2021

1.4.161

Mar 25, 2021

1.4.160

Mar 27, 2021

1.4.159

Mar 27, 2021

1.4.158

Mar 27, 2021

1.4.157

Mar 27, 2021

1.4.156

Mar 27, 2021

1.4.155

Mar 27, 2021

1.4.154

Mar 27, 2021

1.4.153

Mar 27, 2021

1.4.152

Mar 27, 2021

1.4.151

Mar 27, 2021

1.4.150

Mar 27, 2021

1.4.149

Mar 27, 2021

1.4.148

Mar 27, 2021

1.4.147

Mar 27, 2021

1.4.146

Mar 27, 2021

1.4.145

Mar 27, 2021

1.4.144

Mar 27, 2021

1.4.143

Mar 27, 2021

1.4.142

Sep 13, 2020

1.4.141

Sep 5, 2020

1.4.140

Aug 6, 2020

1.4.139

Aug 4, 2020

1.4.138

Jul 22, 2020

1.4.137

Jul 22, 2020

1.4.136

Jul 15, 2020

1.4.135

Jul 12, 2020

1.4.134

Jun 26, 2020

1.4.133

Jun 18, 2020

1.4.132

Jun 14, 2020

1.4.131

May 20, 2020

1.4.130

May 17, 2020

1.4.129

May 12, 2020

1.4.128

May 7, 2020

1.4.127

Apr 24, 2020

1.4.126

Apr 21, 2020

1.4.125

Apr 18, 2020

1.4.124

Apr 17, 2020

1.4.123

Apr 16, 2020

1.4.122

Apr 12, 2020

1.4.121

Apr 10, 2020

1.4.120

Apr 6, 2020

1.4.119

Apr 1, 2020

1.4.118

Mar 20, 2020

1.4.117

Mar 20, 2020

1.4.116

Mar 2, 2020

1.4.115

Feb 29, 2020

1.4.114

Feb 27, 2020

1.4.113

Feb 13, 2020

1.4.112

Jan 22, 2020

1.4.111

Jan 21, 2020

1.4.110

Jan 19, 2020

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Hashes for reacnetgenerator-1.6.12-cp312-cp312-win_amd64.whl

Hashes for reacnetgenerator-1.6.12-cp312-cp312-win_amd64.whl
Algorithm	Hash digest
SHA256	`27198d4ab5759775663bb6e96fc984bb35a3b5f8795ebeec0d69903446272720`
MD5	`a5d29bf3e48699c35c9000e1009b5e66`
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Hashes for reacnetgenerator-1.6.12-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl

Hashes for reacnetgenerator-1.6.12-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm	Hash digest
SHA256	`88433bc69eea19734a8f47d1bc3a22e6b541ecbcd2aef5c9054138e5a47170db`
MD5	`b4c68836a532c4d0b832d6598e28bb32`
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Hashes for reacnetgenerator-1.6.12-cp312-cp312-macosx_10_9_universal2.whl

Hashes for reacnetgenerator-1.6.12-cp312-cp312-macosx_10_9_universal2.whl
Algorithm	Hash digest
SHA256	`60be922fdfb89aaaafbda655203dfc624ca363010d27ec3dfc50ece087a3fa85`
MD5	`6816cb5349ca8ff2a00bb0daf0a7dd86`
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Hashes for reacnetgenerator-1.6.12-cp311-cp311-win_amd64.whl

Hashes for reacnetgenerator-1.6.12-cp311-cp311-win_amd64.whl
Algorithm	Hash digest
SHA256	`d11db5869f3bdb7b747ab89442a9b90670b9bd27ba0f5966980cbf8e8a7d58de`
MD5	`186553814ad5531630e65e3f620820fd`
BLAKE2b-256	`f5b65d1fece8c1f444292877913a45ae8de1c1087b2d5eaf5bbeb7e1b1f90caa`

Hashes for reacnetgenerator-1.6.12-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl

Hashes for reacnetgenerator-1.6.12-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm	Hash digest
SHA256	`8cb46f0e1d0be008bfcabe32d736aa5d2b272008bf0f360c547dd20200151f1e`
MD5	`6d574dfedd9c518e8fe20ec612fe5511`
BLAKE2b-256	`2f241a9e217fb72af40c2c886024e8777e15bff60eccf1fbd05f133cba53db67`

Hashes for reacnetgenerator-1.6.12-cp311-cp311-macosx_10_9_universal2.whl

Hashes for reacnetgenerator-1.6.12-cp311-cp311-macosx_10_9_universal2.whl
Algorithm	Hash digest
SHA256	`72e73888234c85d2798f38a6c897a32bdb599daeb67c2eaa4b2cdd7bd7410f95`
MD5	`9e905fb1b1cdd009e4ba089bf8e4f6f8`
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Hashes for reacnetgenerator-1.6.12-cp310-cp310-win_amd64.whl

Hashes for reacnetgenerator-1.6.12-cp310-cp310-win_amd64.whl
Algorithm	Hash digest
SHA256	`cf2158797a42e9537c5db0388831e210f17ee8f618b34521f49a51d2f76fc255`
MD5	`a54902a8642f63a165ca56ae8970144e`
BLAKE2b-256	`e4d985d016fb7a280ef1db2ed6ff4ffced9c4abd9f0d5ee2d37039e79ec76af4`

Hashes for reacnetgenerator-1.6.12-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl

Hashes for reacnetgenerator-1.6.12-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm	Hash digest
SHA256	`e7a0c0d8ad4323371260e7d13bb7dd138ceea51730d0143e0bbd102869112136`
MD5	`fedaab52d40b580c7f45821a3fbd4874`
BLAKE2b-256	`a4d66c3858b02f7baf5d68827f5c0173a0491911a758e6f93d7983a35d828636`

Hashes for reacnetgenerator-1.6.12-cp310-cp310-macosx_10_9_universal2.whl

Hashes for reacnetgenerator-1.6.12-cp310-cp310-macosx_10_9_universal2.whl
Algorithm	Hash digest
SHA256	`abc2f7ce9a50d7d08a9044aaae0dfd193b9c7e73391b9251389397bf612bb6b5`
MD5	`baca07b8daa7ffd3a3b2276b008802e6`
BLAKE2b-256	`5935613f2a36b23f924fb8a0aab31c235228eea178fe6869754ff9575e9a6c3d`

Hashes for reacnetgenerator-1.6.12-cp39-cp39-win_amd64.whl

Hashes for reacnetgenerator-1.6.12-cp39-cp39-win_amd64.whl
Algorithm	Hash digest
SHA256	`84b202da065c307d584a118204e7c6253e38673f48730920ab4c51c0dc8c3404`
MD5	`4c59fbc62dd4c74bd4f9e1b021157242`
BLAKE2b-256	`1f2beaf230cd3ed48a23bcc030ecd65d06eb857c7e0412001582e6a56522ea82`

Hashes for reacnetgenerator-1.6.12-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl

Hashes for reacnetgenerator-1.6.12-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm	Hash digest
SHA256	`925323847da8867b0daff81aa6a7b10439f616b441a7abadddef136ba85f77fd`
MD5	`cbd46e133e6ffb26d916f84287ac2ad6`
BLAKE2b-256	`9511e62d4a31928d06ee6d80225e5c4d578c0d050814c36730e4300fe55a9335`

Hashes for reacnetgenerator-1.6.12-cp39-cp39-macosx_10_9_universal2.whl

Hashes for reacnetgenerator-1.6.12-cp39-cp39-macosx_10_9_universal2.whl
Algorithm	Hash digest
SHA256	`fd4750ac5d3a67ea999eb260d53592add6e981f11fcf02fe163ef9ecb8bdb031`
MD5	`6e758702348e39c2de4436c3f229c098`
BLAKE2b-256	`afd20c98c4c765bcb6fee275a6caf593d8889691b49693f5a35c7801cdb9a58f`

Hashes for reacnetgenerator-1.6.12-cp38-cp38-win_amd64.whl

Hashes for reacnetgenerator-1.6.12-cp38-cp38-win_amd64.whl
Algorithm	Hash digest
SHA256	`beb70b97e96a4ede185e93607f88767aeb303db2b9ee984917bf9f28bdf92483`
MD5	`22d49c49496764734df0948fd5bdabec`
BLAKE2b-256	`d50a098d52c2bc2376ea46735f6f0d8d57bbd169cb4acc973b2da649d943be61`

Hashes for reacnetgenerator-1.6.12-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl

Hashes for reacnetgenerator-1.6.12-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm	Hash digest
SHA256	`40cad00a41894aac1da78de64a6c17ad76e7f502d3fde44f0fe369704579bd57`
MD5	`3e03e9a809a554ffe372e161b8df5400`
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Hashes for reacnetgenerator-1.6.12-cp38-cp38-macosx_10_9_universal2.whl

Hashes for reacnetgenerator-1.6.12-cp38-cp38-macosx_10_9_universal2.whl
Algorithm	Hash digest
SHA256	`99ee38220739d17ead09b91481e2877f25680d826c2cedf88769ee7dc15fc463`
MD5	`782cd9290128771239dfba07ae0f1db1`
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Hashes for reacnetgenerator-1.6.12-cp37-cp37m-win_amd64.whl

Hashes for reacnetgenerator-1.6.12-cp37-cp37m-win_amd64.whl
Algorithm	Hash digest
SHA256	`cc95d9535f780061a1b2468d36aeaa4cc1cd771085451dfde8c2819fd49fd8aa`
MD5	`27865cc347d478850d8cce842f2e75a1`
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Hashes for reacnetgenerator-1.6.12-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl

Hashes for reacnetgenerator-1.6.12-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm	Hash digest
SHA256	`1b20c2ac3adfe68a2646e78e4ef4805d08652c3b59461d028a4d539916946c2f`
MD5	`a05a688cd0f7bce4ffa5ed2f105d00cc`
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Hashes for reacnetgenerator-1.6.12-cp37-cp37m-macosx_10_9_x86_64.whl

Hashes for reacnetgenerator-1.6.12-cp37-cp37m-macosx_10_9_x86_64.whl
Algorithm	Hash digest
SHA256	`9cb69d07462eb025c149f315ebe36bad0ffc407957c58e99c81342d150cf6b83`
MD5	`14a538f4a4afac915114b4cf3deb6431`
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