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

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 adsorption mechanism requires the most accurate definition of MOF systems. The application of ab initio molecular dynamics to these extensive periodic systems exceeds current computational capabilities. Consequently, alternative strategies need to be devised to lower computational costs without compromising accuracy. In this work, we construct high dimensional neural network potentials (HDNNP) to describe rotationally and translationally invariant energies and forces of isoreticular metal organic framework (IRMOF) series at the DFT level of accuracy using the fragmentation technique so as to study H2 and CH4 adsorption isotherms considering the flexibility of MOFs during gas adsorption by means of “adsorption relaxation” model in which MD and GCMC simulations were performed simultaneously. Our results showed that classical simulations may diverge from experiments due to the failure of the force field when accounting for flexibility in MOFs whereas our HDNNP follows a much better trend to experimental values. Moreover, we show that the real number of CH4 uptake values of IRMOF-10 can be much more than what classical force field predicts. In addition, adsorption relaxation simulations enable us to characterize behavior of MOF atoms and distribution of gas molecules during the adsorption process, giving the most detailed mechanistic picture.