Two-dimensional Energy Histograms as Features for Machine Learning to Predict Adsorption in Diverse Nanoporous Materials
Authors/Creators
- 1. Northwestern University
- 2. University of Florida
- 3. Georgia Institute of Technology
- 4. Oak Ridge National Laboratory
- 5. University of Minnesota
Description
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.