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

  • Kaihang Shi (Creator)
  • Zhao Li (Creator)
  • Dylan M. Anstine (Creator)
  • Dai Tang (Creator)
  • Coray M. Colina (Creator)
  • David S. Sholl (Creator)
  • Ilja Siepmann (Creator)
  • Randall Q. Snurr (Creator)

Dataset

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.
Date made availableOct 19 2022
PublisherZENODO

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