Datasets for "Machine-Learning-Enhanced Symbolic Regression for Methane Storage Prediction in Covalent Organic Frameworks"

This collection contains the datasets and associated files used in the research presented in the manuscript titled "Machine Learning-Enhanced Symbolic Regression for Methane Storage Prediction in Covalent Organic Frameworks". The datasets are critical for the development and validation of machine learning and symbolic regression models aiming to predict methane storage capacities in covalent organic frameworks (COFs).

Included Datasets:

COF_Data_for_ML.csv: This dataset was utilized for the development of machine learning models.

COF_Data_for_SISSO.csv: This dataset was employed for the development of SISSO-based symbolic regression models.

ML_vs_GCMC.xlsx: This comparative dataset features GCMC-calculated results alongside machine learning predictions.

Feature_Combination.xlsx: This file contains data detailing all the feature combinations explored in the study.

ML_SISSO_GCMC.xlsx: This comparative dataset includes GCMC calculations, SISSO-based symbolic regression model predictions, and ML predictions.

Crystallographic_Properties_of_535k_COFs.xlsx: This consolidated dataset presents the crystallographic properties of 535,293 COFs.

Software Used:

Machine Learning Computations: Scikit-Learn (https://scikit-learn.org/stable/)

GCMC Simulations: RASPA2 (https://github.com/iRASPA/RASPA2)

SISSO Calculations: SISSO toolkit (https://github.com/rouyang2017/SISSO)

Crystallographic property calculations: Zeo++ (https://www.zeoplusplus.org/)

The datasets are provided to enable replication of the study's findings, encourage further research in the field, and facilitate the development of advanced predictive models by the scientific community. Researchers who use these datasets are requested to cite this Zenodo entry as well as the associated paper upon its publication.