Highly porous zirconium-based metal−organic frameworks (MOFs) have been widely studied as materials for sorption and destruction of chemical warfare agents (CWAs). It is important to understand the diffusion of CWAs, their reaction products, and environmental molecules through MOFs to utilize these materials for protection against CWA threats. As a first step toward this goal, we study adsorption and diffusion of acetone in pristine UiO-66. We have chosen to study UiO-66 because it has been demonstrated to be effective for destruction of CWAs and simulants; we use acetone because it is a prototypical polar organic molecule small enough to be expected to diffuse fairly rapidly through nondefective UiO-66. We specifically examine the impact of framework flexibility and hydrogen bonding between acetone and the OH groups on the nodes of the framework on the diffusivity of acetone. We find that inclusion of flexibility is essential for meaningful predictions of diffusion of acetone. We have identified the dynamics of the three linkers making up the triangular window between adjacent pores as the critical factor in controlling diffusion of acetone. We demonstrate from experiments and first-principles calculations that acetone readily hydrogen bonds to UiO-66 framework OH groups. We have modified the classical potential for UiO-66 to accurately model the framework−acetone hydrogen bonds, which are not accounted for in many MOF potentials. We find that hydrogen bonding decreases the diffusivity by about 1 order of magnitude at low loading and about a factor of 3 at high loading. Thus, proper accounting of hydrogen bonding and framework flexibility are both critical for obtaining physically realistic values of diffusivities for acetone and similar-sized polar molecules in UiO-66.

Modeling of diffusion of adsorbates through porous materials with atomistic molecular dynamics (MD) can be a challenging task if the flexibility of the adsorbent needs to be included. This is because potentials need to be developed that accurately account for the motion of the adsorbent in response to the presence of adsorbate molecules. In this work, we show that it is possible to use accurate machine learning atomistic potentials for metal−organic frameworks in concert with classical potentials for adsorbates to accurately compute diffusivities though a hybrid potential approach. As a proof-of-concept, we have developed an accurate deep learning potential (DP) for UiO-66, a metal− organic framework, and used this DP to perform hybrid potential simulations, modeling diffusion of neon and xenon through the crystal. The adsorbate−adsorbate interactions were modeled with Lennard−Jones (LJ) potentials, the adsorbent−adsorbent interactions were described by the DP, and the adsorbent−adsorbate interactions used LJ cross-interactions. Thus, our hybrid potential allows for adsorbent−adsorbate interactions with classical potentials but models the response of the adsorbent to the presence of the adsorbate through near-DFT accuracy DPs. This hybrid approach does not require refitting the DP for new adsorbates. We calculated self-diffusion coefficients for Ne in UiO-66 from DFT-MD, our hybrid DP/LJ approach, and from two different classical potentials for UiO-66. Our DP/LJ results are in excellent agreement with DFT-MD. We modeled diffusion of Xe in UiO-66 with DP/LJ and a classical potential. Diffusion of Xe in UiO-66 is about a factor of 30 slower than that of Ne, so it is not computationally feasible to compute Xe diffusion with DFT-MD. Our hybrid DP−classical potential approach can be applied to other MOFs and other adsorbates, making it possible to use an accurate DP generated from DFT simulations of an empty adsorbent in concert with existing classical potentials for adsorbates to model adsorption and diffusion within the porous material, including adsorbate-induced changes to the framework.