Predicting Ion Diffusion from the Shape of Potential Energy Landscapes

Hannes Gustafsson; Melania Kozdra; Berend Smit; Senja Barthel; Amber Mace

doi:10.26434/chemrxiv-2023-0cdjv

Predicting Ion Diffusion from the Shape of Potential Energy Landscapes

Hannes Gustafsson, Melania Kozdra, Berend Smit, Senja Barthel, Amber Mace^*

^*Corresponding author for this work

Mathematics

Research output: Contribution to Journal › Article › Academic › peer-review

Abstract

We present an efficient method to compute diffusion coefficients of multiparticle 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 1 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 the density functional level of theory.

Original language	English
Pages (from-to)	18-29
Number of pages	12
Journal	Journal of Chemical Theory and Computation
Volume	20
Issue number	1
Early online date	19 Dec 2023
DOIs	https://doi.org/10.26434/chemrxiv-2023-0cdjv https://doi.org/10.1021/acs.jctc.3c01005
Publication status	Published - 19 Dec 2023

Bibliographical note

Funding Information:
The authors thank the Swedish Research Council (registration no. 2019-05366), the Swedish Energy Agency (project 50098-1), the Center for Applied Mathematics (CIM) at Uppsala University, and the Swedish National Strategic e-Science programme (eSSENCE) for financial support. The calculations were performed on resources provided by the National Academic Infrastructure for Supercomputing in Sweden (NAISS) at NSC. The authors also thank Leonid Kahle and Nicola Marzari for providing the single-particle Pinball grids and collaborating within NCCR-MARVEL. The initial phase of this work was supported by the NCCR MARVEL, a National Centre of Competence in Research, funded by the Swiss National Science Foundation (grant number 205602).

Publisher Copyright:
© 2023 The Authors. Published by American Chemical Society

Funding

The authors thank the Swedish Research Council (registration no. 2019-05366), the Swedish Energy Agency (project 50098-1), the Center for Applied Mathematics (CIM) at Uppsala University, and the Swedish National Strategic e-Science programme (eSSENCE) for financial support. The calculations were performed on resources provided by the National Academic Infrastructure for Supercomputing in Sweden (NAISS) at NSC. The authors also thank Leonid Kahle and Nicola Marzari for providing the single-particle Pinball grids and collaborating within NCCR-MARVEL. The initial phase of this work was supported by the NCCR MARVEL, a National Centre of Competence in Research, funded by the Swiss National Science Foundation (grant number 205602).

Funders	Funder number
National Center of Competence in Research Materials’ Revolution: Computational Design and Discovery of Novel Materials
Basque Center for Applied Mathematics
National Supercomputer Centre, Linköpings Universitet
Uppsala Universitet
NCCR-MARVEL
NCCR Catalysis
Swiss National Science Foundation	26434, 205602
Energimyndigheten	50098-1
Vetenskapsrådet	2019-05366

Keywords

energy materials
crystalline solid-state ion conductor
diffusion
computational chemistry
topological analysis
graph based analysis
geometric analysis

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.26434/chemrxiv-2023-0cdjvLicence: Unspecified
10.1021/acs.jctc.3c01005Licence: CC BY

Persistent URL (handle)

Persistent link

Cite this

@article{da43790b1fb84f9b9523a0bff1038daa,

title = "Predicting Ion Diffusion from the Shape of Potential Energy Landscapes",

abstract = "We present an efficient method to compute diffusion coefficients of multiparticle 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 1 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 the density functional level of theory.",

keywords = "energy materials, crystalline solid-state ion conductor, diffusion, computational chemistry, topological analysis, graph based analysis, geometric analysis",

author = "Hannes Gustafsson and Melania Kozdra and Berend Smit and Senja Barthel and Amber Mace",

note = "Funding Information: The authors thank the Swedish Research Council (registration no. 2019-05366), the Swedish Energy Agency (project 50098-1), the Center for Applied Mathematics (CIM) at Uppsala University, and the Swedish National Strategic e-Science programme (eSSENCE) for financial support. The calculations were performed on resources provided by the National Academic Infrastructure for Supercomputing in Sweden (NAISS) at NSC. The authors also thank Leonid Kahle and Nicola Marzari for providing the single-particle Pinball grids and collaborating within NCCR-MARVEL. The initial phase of this work was supported by the NCCR MARVEL, a National Centre of Competence in Research, funded by the Swiss National Science Foundation (grant number 205602). Publisher Copyright: {\textcopyright} 2023 The Authors. Published by American Chemical Society",

year = "2023",

month = dec,

day = "19",

doi = "10.26434/chemrxiv-2023-0cdjv",

language = "English",

volume = "20",

pages = "18--29",

journal = "Journal of Chemical Theory and Computation",

issn = "1549-9618",

publisher = "American Chemical Society",

number = "1",

}

TY - JOUR

T1 - Predicting Ion Diffusion from the Shape of Potential Energy Landscapes

AU - Gustafsson, Hannes

AU - Kozdra, Melania

AU - Smit, Berend

AU - Barthel, Senja

AU - Mace, Amber

N1 - Funding Information: The authors thank the Swedish Research Council (registration no. 2019-05366), the Swedish Energy Agency (project 50098-1), the Center for Applied Mathematics (CIM) at Uppsala University, and the Swedish National Strategic e-Science programme (eSSENCE) for financial support. The calculations were performed on resources provided by the National Academic Infrastructure for Supercomputing in Sweden (NAISS) at NSC. The authors also thank Leonid Kahle and Nicola Marzari for providing the single-particle Pinball grids and collaborating within NCCR-MARVEL. The initial phase of this work was supported by the NCCR MARVEL, a National Centre of Competence in Research, funded by the Swiss National Science Foundation (grant number 205602). Publisher Copyright: © 2023 The Authors. Published by American Chemical Society

PY - 2023/12/19

Y1 - 2023/12/19

N2 - We present an efficient method to compute diffusion coefficients of multiparticle 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 1 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 the density functional level of theory.

AB - We present an efficient method to compute diffusion coefficients of multiparticle 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 1 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 the density functional level of theory.

KW - energy materials

KW - crystalline solid-state ion conductor

KW - diffusion

KW - computational chemistry

KW - topological analysis

KW - graph based analysis

KW - geometric analysis

UR - http://www.scopus.com/inward/record.url?scp=85181026972&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85181026972&partnerID=8YFLogxK

U2 - 10.26434/chemrxiv-2023-0cdjv

DO - 10.26434/chemrxiv-2023-0cdjv

M3 - Article

C2 - 38113514

AN - SCOPUS:85181026972

SN - 1549-9618

VL - 20

SP - 18

EP - 29

JO - Journal of Chemical Theory and Computation

JF - Journal of Chemical Theory and Computation

IS - 1

ER -

Predicting Ion Diffusion from the Shape of Potential Energy Landscapes

Abstract

Bibliographical note

Funding

Keywords

UN SDGs

Access to Document

Persistent URL (handle)

Other files and links

Fingerprint

Understanding Mobile Particles in Solid-State Materials: From the Perspective of Potential Energy Surfaces

Computationally efficient DFT-based sampling of ion diffusion in crystalline solids

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

Cite this