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Combined Deep Learning and Classical Potential Approach for Modeling Diffusion in UiO-66.

Siddarth K Achar1, Jacob J Wardzala2, Leonardo Bernasconi3

  • 1Computational Modeling & Simulation Program, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States.

Journal of Chemical Theory and Computation
|June 2, 2022
PubMed
Summary
This summary is machine-generated.

Accurate atomistic potentials for metal-organic frameworks (MOFs) enable precise diffusion modeling. A hybrid approach using machine learning potentials and classical force fields accurately predicts adsorbate diffusion, even for flexible MOFs.

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Chemical Engineering

Background:

  • Atomistic molecular dynamics (MD) simulations are crucial for modeling adsorbate diffusion in porous materials.
  • Including adsorbent flexibility in MD simulations requires complex potential development.
  • Accurate modeling of adsorbate-induced framework motion is computationally challenging.

Purpose of the Study:

  • To develop and validate a hybrid potential approach for accurate MD simulations of adsorbate diffusion in flexible metal-organic frameworks (MOFs).
  • To demonstrate the feasibility of using machine learning potentials for MOFs with classical potentials for adsorbates.
  • To enable efficient and accurate computation of diffusion coefficients for various adsorbates in MOFs.

Main Methods:

  • Developed a deep learning potential (DP) for the UiO-66 metal-organic framework.
  • Employed a hybrid potential approach combining the DP for adsorbent-adsorbent interactions with Lennard-Jones (LJ) potentials for adsorbate-adsorbate and adsorbent-adsorbate interactions.
  • Performed MD simulations to calculate self-diffusion coefficients for Neon (Ne) and Xenon (Xe) in UiO-66.

Main Results:

  • The hybrid DP/LJ approach accurately reproduced self-diffusion coefficients for Ne in UiO-66, showing excellent agreement with DFT-MD results.
  • Successfully modeled the diffusion of Xe in UiO-66, a computationally intensive task for DFT-MD due to its slower diffusion rate.
  • Demonstrated that the hybrid approach does not require refitting the DP for new adsorbates.

Conclusions:

  • The hybrid potential approach offers a computationally efficient and accurate method for modeling adsorbate diffusion in flexible MOFs.
  • This method allows for the inclusion of adsorbate-induced framework relaxations without the need for developing new, complex potentials for each adsorbate-adsorbent pair.
  • The developed methodology is applicable to other MOFs and adsorbates, advancing the understanding of gas adsorption and transport in porous materials.