From Ideal Models to Structural Ensembles: Predictive Realism for Molecular Simulation of Porous Materials

Area of Science:

• Materials Science
• Computational Chemistry
• Chemical Engineering

Background:

• Traditional molecular simulations of porous materials use idealized, periodic structures.
• This approach simplifies studies of adsorption, diffusion, and screening but often fails to represent real materials accurately.
• Experimental and computational data reveal that defects, chemistry, and interfaces create significant material heterogeneity.

Purpose of the Study:

• To introduce 'predictive realism' for molecular simulations of porous materials.
• To argue that the definition of the structural object, not simulation methods, is the key obstacle to accurate prediction.
• To establish a framework for simulations that represent physically relevant material variability.

Main Methods:

• Utilizing recent experimental and simulation results to demonstrate the impact of structural variability.
• Employing ensemble-based descriptions to capture multiple, physically plausible structural realizations.
• Focusing on selective inclusion of relevant variability, quantitative validation, and reproducible workflows.

Main Results:

• Multiple structural realizations of porous materials can lead to different measurable responses (adsorption, transport, mechanical stability).
• Ensemble-based simulations can align molecular modeling with experimental measurements.
• The proposed framework emphasizes realistic representation over maximal complexity.

Conclusions:

• Predictive realism requires molecular simulations to represent physically relevant variability affecting observables.
• Ensemble descriptions provide a path toward more realistic and reliable simulations for porous materials under working conditions.
• This approach offers a consistent standard for future molecular simulation studies of real materials.