Automated Multiscale Approach to Predict Self-Diffusion from a Potential Energy Field

Amber Mace, Senja Barthel, Berend Smit

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

For large-scale screening studies there is a need to estimate the diffusion of gas molecules in nanoporous materials more efficiently than (brute force) molecular dynamics. In particular for systems with low diffusion coefficients molecular dynamics can be prohibitively expensive. An alternative is to compute the hopping rates between adsorption sites using transition state theory. For large-scale screening this requires the automatic detection of the transition states between the adsorption sites along the different diffusion paths. Here an algorithm is presented that analyzes energy grids for the moving particles. It detects the energies at which diffusion paths are formed, together with their directions. This allows for easy identification of nondiffusive systems. For diffusive systems, it partitions the grid coordinates assigned to energy basins and transitions states, permitting a transition state theory based analysis of the diffusion. We test our method on CH 4 diffusion in zeolites, using a standard kinetic Monte Carlo simulation based on the output of our grid analysis. We find that it is accurate, fast, and rigorous without limitations to the geometries of the diffusion tunnels or transition states.

Original languageEnglish
Pages (from-to)2127-2141
Number of pages15
JournalJournal of Chemical Theory and Computation
Volume15
Issue number4
DOIs
Publication statusPublished - 9 Apr 2019
Externally publishedYes

Fingerprint

Potential energy
potential energy
grids
screening
Molecular dynamics
Screening
molecular dynamics
adsorption
Zeolites
Adsorption
zeolites
tunnels
energy
partitions
diffusion coefficient
Tunnels
methylidyne
Gases
output
kinetics

Cite this

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Automated Multiscale Approach to Predict Self-Diffusion from a Potential Energy Field. / Mace, Amber; Barthel, Senja; Smit, Berend.

In: Journal of Chemical Theory and Computation, Vol. 15, No. 4, 09.04.2019, p. 2127-2141.

Research output: Contribution to JournalArticleAcademicpeer-review

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