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Updated: Jan 12, 2026

Synthesis and Characterization of Functionalized Metal-organic Frameworks
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Sudheesh Kumar Ethirajan1, Ambarish R Kulkarni1

  • 1Department of Chemical Engineering, University of California, Davis, California 95616, United States.

Journal of Chemical Theory and Computation
|October 30, 2025
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Summary
This summary is machine-generated.

This study introduces a new active learning method to train machine learning potentials for studying rare events in nanoporous materials. The enhanced sampling approach captures complex diffusion mechanisms previously inaccessible with standard simulations.

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Chemical Engineering

Background:

  • Machine learning potentials (MLPs) enable accurate simulations of nanoporous materials but struggle with rare events due to limited training data from near-equilibrium states.
  • Studying high-energy, off-equilibrium configurations is crucial for understanding rare events but is computationally expensive with traditional methods.

Purpose of the Study:

  • To develop and demonstrate a novel active learning strategy for training MLPs capable of simulating rare events in nanoporous materials.
  • To overcome data scarcity issues in training MLPs for complex physicochemical phenomena.

Main Methods:

  • Implemented an active learning curriculum utilizing the On-the-fly Probability Enhanced Sampling (OPES) method.
  • Applied time-dependent OPES biases with collective variables to systematically explore the potential energy surface of imidazole diffusion in the SALEM-2 metal-organic framework (MOF).
  • Performed molecular dynamics (MD) simulations with near-density functional theory (DFT) accuracy.

Main Results:

  • Successfully captured imidazole diffusion through both 6-membered and a previously unreported 4-membered window in the MOF.
  • Observed a novel ring-opening mechanism involving transient Zn-N bond dissociation, which is beyond the scope of classical force fields and DFT.
  • Achieved nanosecond-scale MD simulations with high accuracy.

Conclusions:

  • Enhanced sampling methods, like OPES, are effective in overcoming data scarcity for training MLPs to study rare events in nanoporous materials.
  • The developed approach enables the accurate simulation of complex, off-equilibrium processes crucial for materials discovery and design.