Analysis of CH4 uptake over MOFs using data mining tools.

A database containing 2224 data points for CH4 storage or delivery in metal organic frameworks (MOFs) was analysed using machine learning tools to extract knowledge for generalization. The database was first reviewed to observe the basic trends and patterns. It was then analysed using decision trees and artificial neural networks (ANN) to extract hidden information and develop rules and heuristics for the future studies. Five-fold cross validations were used in each analysis to test the validity of the models with data not seen before. Decision tree analyses were carried out using six user defined descriptors and two structural properties, separately. The crystal structure and the total degree of unsaturation were found to be the effective user defined descriptors, whereas the pore volume and maximum pore diameter, as structural properties, were sufficient to determine the MOFs having high CH4 storage capacity. Moreover, a high pore volume is always required as expected. In ANN analyses, models were also performed by using user defined descriptors and structural properties separately. It was observed that the user defined descriptors were not sufficient to describe the CH4 storage capacity of MOFs, whereas the structural properties, especially led to accurate CH4 storage predictions with an RMSE of 26.8 and an R2 of 0.92 for testing.