Coarse graining of force fields for metal-organic frameworks

Area of Science:

• Computational Chemistry
• Materials Science
• Nanotechnology

Background:

• Atomistic force fields for Metal-Organic Frameworks (MOFs) are computationally expensive.
• Simulating large-scale MOF properties requires more efficient models.

Purpose of the Study:

• To develop a coarse-grained force field (MOF-FF-CGNB) for MOFs.
• To enable simulations of larger MOF systems and bridge the gap to the mesoscale.

Main Methods:

• Adapted a genetic algorithm for optimizing force field parameters from quantum mechanics data.
• Developed a maximally coarse-grained model for HKUST-1 using single beads for vertices.
• Incorporated non-bonded interactions with a modified Buckingham potential.

Main Results:

• The MOF-FF-CGNB model semi-quantitatively predicts local deformation energies and bulk properties like elastic constants.
• The model does not reproduce the negative thermal expansion of HKUST-1.
• Simulations of larger systems (tens of nanometers) become feasible, showing mechanical properties without artifacts.

Conclusions:

• Coarse-graining significantly enhances simulation size for MOFs, enabling mesoscale studies.
• The MOF-FF-CGNB model offers a balance between detail and computational efficiency for MOF simulations.
• This approach facilitates the study of MOF mechanical properties at larger scales.