Modeling Gas Adsorption and Mechanistic Insights into Flexibility in Isoreticular Metal-Organic Frameworks Using High-Dimensional Neural Network Potentials

Abstract

Metal-organic frameworks (MOFs), known for their remarkable porous and well-organized structures, have found extensive use in various applications, including gas storage. Predicting the bulk properties from atomistic simulations as well as gas uptakes and the adsorption mechanism requires the most accurate definition of MOF systems. The application of ab initio molecular dynamics to these extensive periodic systems exceeds the current computational capabilities. Consequently, alternative strategies need to be devised to decrease computational costs without compromising accuracy. In this work, we construct high-dimensional neural network potentials (HDNNPs) to describe rotationally and translationally invariant energies and forces of isoreticular metal-organic framework (IRMOF) series at the density functional theory level of accuracy using a fragmentation technique to study H2 and CH4 adsorption isotherms by means of an adsorption-relaxation model in which molecular dynamics and grand canonical Monte Carlo simulations were performed simultaneously. Herein, for the first time, we report that HDNNPs could be utilized for such simulations with excellent agreement with experimental values. We also report that the UFF4MOF force field may not be suitable for adsorption-relaxation simulations. In addition, we show that the real number of CH4 uptake values of IRMOF-10 under the extreme conditions could be much greater than what the classical force field predicts. Adsorption-relaxation simulations enable us to characterize the behavior of MOF atoms and the distribution of gas molecules during the adsorption process, giving the most detailed mechanistic picture.