Model reduction for molecular diffusion in nanoporous media

TL;DR

This paper introduces a dimensionality reduction method using 1D Langevin dynamics to efficiently estimate gas diffusion in nanoporous materials, significantly simplifying complex 3D atomistic simulations.

Contribution

It presents a novel approach to model molecular diffusion in porous media by reducing 3D atomistic interactions to an effective 1D diffusion problem, enabling faster and scalable predictions.

Findings

1D Langevin models accurately predict diffusion coefficients.

The approach works across various temperatures and gas densities.

Effective for modeling transport in nanotubes and potentially other porous materials.

Abstract

Porous materials are widely used for applications in gas storage and separation. The diffusive properties of a variety of gases in porous media can be modeled using molecular dynamics simulations that can be computationally demanding depending on the pore geometry, complexity and amount of gas adsorbed. We explore a dimensionality reduction approach for estimating the self-diffusion coefficient of gases in simple pores using Langevin dynamics, such that the three-dimensional (3D) atomistic interactions that determine the diffusion properties of realistic systems can be reduced to an effective one-dimensional (1D) diffusion problem along the pore axis. We demonstrate the approach by modeling the transport of nitrogen molecules in single-walled carbon nanotubes of different radii, showing that 1D Langevin models can be parametrized with a few single-particle 3D atomistic simulations. The reduced 1D model predicts accurate diffusion coefficients over a broad range of temperatures and gas densities. Our work paves the way for studying the diffusion process of more general porous materials as zeolites or metal-organics frameworks with effective models of reduced complexity.