Combined Deep Learning and Classical Potential Approach for Modeling Diffusion 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.