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Understanding Dynamics of Nanocluster-Organic Frameworks and Gas Diffusion from Machine Learning Potential-Based

Animesh Karmakar1, Dhananjay1, Tarak Karmakar1

  • 1Department of Chemistry, Indian Institute of Technology Delhi, New Delhi-110016, India.

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This study models nanocluster-organic frameworks (NOFs) and oxygen diffusion using machine learning. It reveals O2 localizes near linkers, with feasible room-temperature diffusion, advancing NOF material understanding.

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Chemical Engineering

Background:

  • Nanocluster-organic frameworks (NOFs) are advanced photofunctional materials with applications in sensing and optoelectronics.
  • Their luminescence properties are sensitive to gases like oxygen (O2) and volatile organic compounds.
  • Understanding the atomistic structure and small molecule diffusion mechanisms within NOFs is crucial but remains challenging.

Purpose of the Study:

  • To develop accurate machine learning potentials for modeling a specific NOF, [Ag12(StBu)8(CF3COO)4(bpy)4]n.
  • To investigate the structural evolution and dynamics of this NOF using molecular dynamics simulations.
  • To elucidate the diffusion mechanism and free energy landscape of O2 gas within the NOF pores.

Main Methods:

  • Development of machine learning potentials for accurate atomistic modeling of NOFs.
  • Molecular dynamics simulations to study NOF structure and dynamics.
  • On-the-fly probability-enhanced sampling simulations for O2 diffusion and free energy surface construction.

Main Results:

  • Accurate modeling of the flexible NOF structure and dynamics was achieved.
  • O2 gas was observed to predominantly localize around the bipyridine linker within the NOF.
  • A pore-to-pore diffusion barrier of approximately 16-18 kJ/mol for O2 was determined, indicating feasible room-temperature diffusion.

Conclusions:

  • This work presents the first integrated approach for modeling a flexible NOF and its gas diffusion with DFT-level accuracy.
  • The findings provide insights into the gas interaction and transport mechanisms in NOFs.
  • The developed methodology can be applied to study other porous materials and guest molecule interactions.