EnergyGrid Machine Learning Project
Objective
Predict adsorption characteristics of MOFs using machine learning based on Energy grid and the histograms
Creator&Maintainer
Benjamin J. Bucior
Zhao Li
Funding
This work was supported by the U.S. Department of Energy, Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences and Biosciences through the Nanoporous Materials Genome Center under Award Numbers DEFG02-12ER16362 and DE-FG02-17ER16362.
Citation
1. Bucior, B. J.; Bobbitt, N. S.; Islamoglu, T.; Goswami, S.; Gopalan, A.; Yildirim, T.; Farha, O. K.; Bagheri, N.; Snurr, R. Q. Energy-Based Descriptors to Rapidly Predict Hydrogen Storage in Metal–Organic Frameworks. Mol. Syst. Des. Eng. 2019, 4 (1), 162–174.
2. Li, Z.; Bucior, B. J.; Chen H., Haranczyk, M.; Siepmann, J. I.; Snurr, R. Q. Machine Learning Using Host/Guest Energy Histograms to Predict Adsorption in Metal-Organic Frameworks: Application to Short Alkanes and Xe/Kr Mixtures. J. Chem. Phys. In Press.
Usage
For detailed usages of this code, please go to the R/ source code directory.