Metadata-Version: 2.1
Name: CoREMOF-tools
Version: 0.0.2
Summary: Python API for CoRE MOF 2024 DB
Home-page: https://github.com/mtap-research/CoRE-MOF-Tools
Author: Guobin Zhao
Author-email: sxmzhaogb@gmail.com
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Build Tools
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Requires-Python: >=3.9, <4
Description-Content-Type: text/markdown
Requires-Dist: pymatgen
Requires-Dist: ase
Requires-Dist: juliacall
Requires-Dist: molSimplify
Requires-Dist: PACMAN-charge
Requires-Dist: cloudpickle
Requires-Dist: matminer
Requires-Dist: xgboost
Requires-Dist: scikit-learn (==1.3.2)
Requires-Dist: mofchecker
Provides-Extra: zeopp
Requires-Dist: zeopp-lsmo ; extra == 'zeopp'

<img src="https://raw.githubusercontent.com/mtap-research/CoRE-MOF-Tools/main/figs/logo.png" alt="coremof2024" width="500">
                                 
#### Installation                                                                                    
This API includes tools developed to collect, curate, and classify Computation-Ready, Experimental MOF database.    
You need to install the [CSD software and python API](https://downloads.ccdc.cam.ac.uk/documentation/API/installation_notes.html) before downloading the full CoRE MOF database.                                                            
For using CoREMOF.calculation.Zeopp, you need to input `conda install -c conda-forge zeopp-lsmo` to install Zeo++.   

#### Examples                                                                                     
Available at [Github](https://github.com/mtap-research/CoRE-MOF-Tools/tree/main/7-data4API/examples) and [CoRE MOF Website](http://www.coremof.org/API) to view examples.                         
                            
                  
#### Citation                                          
- [CoRE MOF](https://chemrxiv.org/engage/chemrxiv/article-details/6757ca12f9980725cf91c7e0): Zhao G, Brabson L, Chheda S, Huang J, Kim H, Liu K, et al. CoRE MOF DB: a curated experimental metal-organic framework database with machine-learned properties for integrated material-process screening. ChemRxiv. 2024; doi:10.26434/chemrxiv-2024-nvmnr.                        
- [Zeo++](https://www.sciencedirect.com/science/article/pii/S1387181111003738): T.F. Willems, C.H. Rycroft, M. Kazi, J.C. Meza, and M. Haranczyk, Algorithms and tools for high-throughput geometry- based analysis of crystalline porous materials, Microporous and Mesoporous Materials, 149 (2012) 134-141.                            
- []
