Metadata-Version: 2.1
Name: mofclassifier
Version: 0.1.2
Summary: A Machine Learning Approach for Validating Computation-Ready Metal-Organic Frameworks
Home-page: https://github.com/Chung-Research-Group/MOFClassifier
Author: Guobin Zhao
Author-email: sxmzhaogb@gmai.com
License: CC-BY-4.0
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Topic :: Scientific/Engineering :: Chemistry
Classifier: Programming Language :: Python :: 3.9
Requires-Python: >=3.9, <4
Description-Content-Type: text/markdown
Requires-Dist: ase
Requires-Dist: numpy==1.26.4
Requires-Dist: torch==2.7.0
Requires-Dist: Pymatgen==2024.8.9
Requires-Dist: scikit-learn==1.3.2
Requires-Dist: tqdm==4.67.1
Requires-Dist: pandas==2.2.3

## MOFClassifier: A Machine Learning Approach for Validating Computation-Ready Metal-Organic Frameworks

<div align="center">
  <img src="https://raw.githubusercontent.com/sxm13/pypi-dev/main/logos/mofclassifier.png" alt="mofclassifier logo" width="500"/>
</div>                                                                                                                                        

[![Static Badge](https://img.shields.io/badge/arXiv.2506.14845v1-brightgreen?style=flat)](https://arxiv.org/abs/2506.14845)
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### Installation 
                                     
```sh
pip install MOFClassifier
```

### Examples                                                                                                     
```python
from MOFClassifier import CLscore
result = CLscore.predict(root_cif="./example.cif", model="core")
```
-  **root_cif**: the path of your structure
-  **model**: the model name: a. "core": training with CoRE MOF DB; b. "qsp": training with CoRE MOF DB and QMOF DB; c. "h": training with ToBaCCo (Hypothetical MOFs)
-  **result**: a. cifid: the name of structure; b. all_score: the CLscore predicted by 100 models (bags); c. mean_score: the mean CLscore of CLscores                                                 

```python
from MOFClassifier import CLscore
results = CLscore.predict_batch(root_cifs=["./example1.cif""./example2.cif","./example3.cif"], model="core", batch_size=512)
```
-  **root_cifs**: the path of your structures
-  **model**: the model name: a. "core": training with CoRE MOF DB; b. "qsp": training with CoRE MOF DB and QMOF DB; c. "h": training with ToBaCCo (Hypothetical MOFs)
-  **batch_size**: the number of samples
-  **results**: a. cifid: the name of structure; b. all_score: the CLscore predicted by 100 models (bags); c. mean_score: the mean CLscore of CLscores  
                                                                                
### Citation                                          
[Guobin Zhao, Pengyu Zhao and Yongchul G. Chung. ***Journal of the American Chemical Society***, 2025, 147, 37, 33343–33349. DOI: 10.1021/jacs.5c10126](https://pubs.acs.org/doi/10.1021/jacs.5c10126)


### Acknowledgments
We thank [henk789](https://github.com/henk789) for contribution to batch prediction.
