Efficient Modeling of Water Adsorption in MOFs Using Interpolated Transition Matrix Monte Carlo

Area of Science:

• Materials Science
• Computational Chemistry
• Environmental Science

Background:

• Climate change necessitates innovative solutions for potable water access.
• Atmospheric water harvesting (AWH) using metal-organic frameworks (MOFs) is a promising technology.
• Standard computational methods struggle with water adsorption simulations in MOFs.

Purpose of the Study:

• To develop an efficient computational methodology for screening MOFs for water adsorption.
• To overcome limitations of standard numerical methods in simulating water adsorption.
• To enable rapid, large-scale computational screening of sorbents.

Main Methods:

• Employed grand canonical transition matrix Monte Carlo (GC-TMMC) methodology.
• Developed an efficient interpolation scheme to reduce simulation requirements.
• Utilized extrapolation of the free energy landscape for property prediction.

Main Results:

• The GC-TMMC method with interpolation significantly reduces simulations while maintaining accuracy.
• Accurate prediction of water adsorption properties across various pressures and temperatures.
• Demonstrated applicability to MOF-303, MOF-LA2-1, and NU-1000.

Conclusions:

• The proposed methodology enables rapid, large-scale computational screening of sorbents.
• This versatile approach provides rich thermodynamic data for sorbent discovery.
• A modified RASPA2 code and Python library are provided to facilitate adoption.