化学和材料科学的机器学习和信息学程序包
chemml的Python项目详细描述
化学药品
chemml是一个机器学习和信息学程序套件，用于分析、挖掘和建模化学和材料数据。 有关详细信息，请查看ChemML website。
- chemml文档：https://hachmannlab.github.io/chemml
代码设计：
chemml是用python 3编程语言开发的，它使用了大量的数据分析和ml库（可以通过anaconda发行版访问）以及特定于域的库。 开发遵循严格的模块化和面向对象的设计，使整个代码尽可能灵活和通用。
library的格式类似于scikit learn这样的知名库。Chemml将很快上市 通过ChemEco提供的图形用户界面。 chemeco是一个无需编码的通用数据挖掘框架。它还与许多为 大规模化学数据集的表示、预处理、分析、挖掘和建模。
最新版本：
- 要查找最新版本和发行历史，请单击here
安装和依赖项：
您可以通过pip从pypi下载chemml。
pip install chemml --user -U
下面是将与chemml一起安装的外部库的列表：
- 努比
- 熊猫
- 张量流
- 路缘石
- SCIKIT学习
- matplotlib
- 肖伯恩
- lxml
由于conda安装还不能用于chemml，我们建议在conda虚拟环境中安装rdkit和openbabel 正在安装Chemml。为此，您需要遵循conda安装程序：
conda create --name my_chemml_env python=3.6
source activate my_chemml_env
conda install -c conda-forge openbabel rdkit tensorflow keras
pip install chemml
引文：
请引用chemml的用法：
Main citation:
@article{chemml2019,
author = {Haghighatlari, Mojtaba and Vishwakarma, Gaurav and Altarawy, Doaa and Subramanian, Ramachandran and Kota, Bhargava Urala and Sonpal, Aditya and Setlur, Srirangaraj and Hachmann, Johannes},
journal = {ChemRxiv},
pages = {8323271},
title = {ChemML: A Machine Learning and Informatics Program Package for the Analysis, Mining, and Modeling of Chemical and Materials Data},
doi = {10.26434/chemrxiv.8323271.v1},
year = {2019}
}
Other references:
@article{chemml_review2019,
author = {Haghighatlari, Mojtaba and Hachmann, Johannes},
doi = {https://doi.org/10.1016/j.coche.2019.02.009},
issn = {2211-3398},
journal = {Current Opinion in Chemical Engineering},
month = {jan},
pages = {51--57},
title = {Advances of machine learning in molecular modeling and simulation},
volume = {23},
year = {2019}
}
@article{Hachmann2018,
author = {Hachmann, Johannes and Afzal, Mohammad Atif Faiz and Haghighatlari, Mojtaba and Pal, Yudhajit},
doi = {10.1080/08927022.2018.1471692},
issn = {10290435},
journal = {Molecular Simulation},
number = {11},
pages = {921--929},
title = {Building and deploying a cyberinfrastructure for the data-driven design of chemical systems and the exploration of chemical space},
volume = {44},
year = {2018}
}
许可证：
Chemml版权所有（c）2014-2018 Johannes Hachmann和Mojtaba Haghatlari，保留所有权利。 chemml是根据3子句bsd许可证（https://opensource.org/licenses/BSD-3-Clause）分发的。
关于我们：
维护人员：
- Johannes Hachmann, hachmann@buffalo.edu
- Mojtaba Haghighatlari
University at Buffalo - The State University of New York (UB)
贡献者：
- Doaa Altarawy (MolSSI): scientific advice and software mentor
- Gaurav Vishwakarma (UB): automated model optimization
- Ramachandran Subramanian (UB): Magpie descriptor library port
- Bhargava Urala Kota (UB): library database
- Aditya Sonpal (UB): debugging
- Srirangaraj Setlur (UB): scientific advice
- Venugopal Govindaraju (UB): scientific advice
- Krishna Rajan (UB): scientific advice
- We encourage any contributions and feedback. Feel free to fork and make pull-request to the "development" branch.
致谢：
- ChemML is based upon work supported by the U.S. National Science Foundation under grant #OAC-1751161 and in part by #OAC-1640867.
- ChemML was also supported by start-up funds provided by UB's School of Engineering and Applied Science and UB's Department of Chemical and Biological Engineering, the New York State Center of Excellence in Materials Informatics through seed grant #1140384-8-75163, and the U.S. Department of Energy under grant #DE-SC0017193.
- Mojtaba Haghighatlari received 2018 Phase-I and 2019 Phase-II Software Fellowships by the Molecular Sciences Software Institute (MolSSI) for his work on ChemML.
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