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Predicting Ion Diffusion from the Shape of Potential Energy Landscapes

pmid: 38113514
pmc: PMC10782449
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.
- Uppsala University Sweden
- École Polytechnique Fédérale de Lausanne EPFL Switzerland
- Free University of Amsterdam Pure VU Amsterdam Netherlands
- Department of Chemistry Austria
- Department of Chemistry Switzerland
energy materials, Oorganisk kemi, diffusion, graph based analysis, Condensed Matter Physics, computational chemistry, geometric analysis, crystalline solid-state ion conductor, Inorganic Chemistry, topological analysis, Electrolytes, Batteries, Structure Database Icsd, SDG 7 - Affordable and Clean Energy, Den kondenserade materiens fysik, Force-Field
energy materials, Oorganisk kemi, diffusion, graph based analysis, Condensed Matter Physics, computational chemistry, geometric analysis, crystalline solid-state ion conductor, Inorganic Chemistry, topological analysis, Electrolytes, Batteries, Structure Database Icsd, SDG 7 - Affordable and Clean Energy, Den kondenserade materiens fysik, Force-Field
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).2 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
