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A hydrogen bond is formed when a weakly positive hydrogen atom already bonded to one electronegative atom (for example, the oxygen in the water molecule) is attracted to another electronegative atom from another polar molecule, such as water (H2O), hydrogen fluoride (HF), or ammonia (NH3). The huge electronegativity difference between the H atom (2.1) and the atom to which it is bonded (4.0 for an F atom, 3.5 for an O atom, or 3.0 for an N atom), combined with the very small size of an H atom...
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Machine learning potential for modelling dynamic hydrogen bond networks in MOF MIL-120.

Xin Jin1, Yutao Li1, Kelian Gaedecke1

  • 1Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingénierie Chimiques, École Polytechnique Fédérale de Lausanne (EPFL) Switzerland berend.smit@epfl.ch.

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|April 15, 2026
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Summary
This summary is machine-generated.

We developed a computational method to accurately simulate gas adsorption in flexible metal-organic frameworks (MOFs). This approach reveals how CO2 adsorption influences the dynamic pore structure of MIL-120, offering insights for MOF design.

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Chemical Engineering

Background:

  • Metal-organic frameworks (MOFs) show promise for gas adsorption and separation.
  • Accurate simulation of gas adsorption in MOFs using DFT methods is computationally challenging.
  • MOFs often exhibit dynamic structural behavior influencing their adsorption properties.

Purpose of the Study:

  • To develop a computational workflow for accurate simulation of gas adsorption in flexible MOFs.
  • To investigate the interplay between CO2 adsorption and the dynamic pore environment in MIL-120.
  • To establish a generalizable strategy for simulating adsorption in dynamic MOF systems.

Main Methods:

  • Fine-tuning a pre-trained MACE potential for accurate interatomic interactions in MOFs.
  • Developing a computational workflow integrating machine learning potentials with molecular simulations.
  • Utilizing accelerated sampling with ML potentials to study CO2 adsorption dynamics.

Main Results:

  • Created accurate machine-learning interatomic potentials for MIL-120, capturing its dynamic structural behavior.
  • Uncovered a strong coupling between CO2 adsorption and the hydrogen-bond network on the MIL-120 pore surface.
  • Observed that CO2 adsorption induces local rearrangements, reshaping the MOF's pore environment.

Conclusions:

  • The developed computational workflow enables accurate simulation of gas adsorption in flexible MOFs.
  • CO2 adsorption significantly impacts the dynamic hydrogen-bond network and pore structure of MIL-120.
  • This strategy offers a generalizable approach for studying adsorption phenomena in other dynamic MOF materials.