Predicting ion diffusion from the shape of potential energy landscapes

18 October 2023, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

We present an efficient method to compute diffusion coefficients of multi-particle systems with strong interactions directly from the geometry and topology of the potential energy field of the migrating particles. The approach is tested on Li-ion diffusion in crystalline inorganic solids, predicting Li-ion diffusion coefficients within one order of magnitude of molecular dynamics simulations at the same level of theory while being several orders of magnitude faster. The speed and transferability of our workflow make it well suited for extensive and efficient screening studies of crystalline solid-state ion conductor candidates and promise to serve as a platform for diffusion prediction even up to density functional level of theory.

Keywords

ion diffusion
transistion state theory
kinetic monte carlo
multi-scale modeling
solid-state electrolytes
Li-ion batteries

Supplementary materials

Title
Description
Actions
Title
Supporting Information: Predicting ion diffusion from the shape of potential energy landscapes
Description
dditional computational details and analysis results.
Actions

Comments

Comments are not moderated before they are posted, but they can be removed by the site moderators if they are found to be in contravention of our Commenting Policy [opens in a new tab] - please read this policy before you post. Comments should be used for scholarly discussion of the content in question. You can find more information about how to use the commenting feature here [opens in a new tab] .
This site is protected by reCAPTCHA and the Google Privacy Policy [opens in a new tab] and Terms of Service [opens in a new tab] apply.
Comment number 1, Senja Barthel: Oct 12, 2024, 09:16

The published version is open access Predicting Ion Diffusion from the Shape of Potential Energy Landscapes Hannes Gustafsson, Melania Kozdra, Berend Smit, Senja Barthel, and Amber Mace Journal of Chemical Theory and Computation 2024 20 (1), 18-29 DOI: 10.1021/acs.jctc.3c01005