Two-dimensional Energy Histograms as Features for Machine Learning to Predict Adsorption in Diverse Nanoporous Materials

This repo contains the supplementary data sets for the to-be-published paper entitled "Two-dimensional Energy Histograms as Features for Machine Learning to Predict Adsorption in Diverse Nanoporous Materials". This repo contains the following data sets: 1. CIF files for amorphous porous materials (activated carbon, hyper-cross-linked polymers, Kerogen, PIMs). 2. Grand canonical Monte Carlo (GCMC) simulation results for single-component adsorption isotherms in ToBaCCo1.0 MOFs and in amorphous porous materials. Gas molecules include Kr, Xe, ethane, propane, butane, n-hexane, and 2,2-dimethylbutane. 3. Textural properties of ToBaCCo1.0 MOFs and amorphous porous materials. 4. Trained machine learning models. R code that can work with these ML models is hosted on GitHub.