---
res:
  bibo_abstract:
  - Disordered materials such as glasses are promising candidates as solid-state electrolytes
    for batteries, but they tend to exhibit low ionic conductivity and current methods
    for investigating and predicting the ionic conductivity of electrolyte materials
    are inefficient. Here, we propose a framework that summarizes the topological
    properties of the electrolyte material’s atomic structure with a weighted graph
    based on periodic boundary conditions. We then compute a variation of the max-flow
    algorithm, a commonly used model in continuum models of transportation and liquid
    flow, on this summary and show that it is compatible with the simulated periodic
    atomic structures. Using molecular dynamics simulations of various glassy solid-state
    electrolyte families including both mobile lithium and sodium ions, we validate
    the approach by showing a positive correlation between the area-normalized maximum
    flow predicted from the static glass structures and the ionic conductivity as
    obtained from the simulations, especially in the case of high-conductivity sulfide
    glass electrolytes. We envision that the developed methodology, which only relies
    on static atomic structure, will accelerate the discovery of highly conductive
    sulfide glassy electrolytes for high-performance all-solid-state batteries.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Matteo
      foaf_name: Pegoraro, Matteo
      foaf_surname: Pegoraro
  - foaf_Person:
      foaf_givenName: Rasmus
      foaf_name: Christensen, Rasmus
      foaf_surname: Christensen
  - foaf_Person:
      foaf_givenName: Søren S.
      foaf_name: Sørensen, Søren S.
      foaf_surname: Sørensen
  - foaf_Person:
      foaf_givenName: Anders R.
      foaf_name: Lindbjerg, Anders R.
      foaf_surname: Lindbjerg
  - foaf_Person:
      foaf_givenName: Tao
      foaf_name: Du, Tao
      foaf_surname: Du
  - foaf_Person:
      foaf_givenName: Yossi
      foaf_name: Bleile, Yossi
      foaf_surname: Bleile
      foaf_workInfoHomepage: http://www.librecat.org/personId=920a7385-7995-11ef-9bfd-8c434cd8f3c2
    orcid: 0000-0002-4861-9174
  - foaf_Person:
      foaf_givenName: Anne Marie
      foaf_name: Svane, Anne Marie
      foaf_surname: Svane
  - foaf_Person:
      foaf_givenName: Christophe A.N.
      foaf_name: Biscio, Christophe A.N.
      foaf_surname: Biscio
  - foaf_Person:
      foaf_givenName: Lisbeth
      foaf_name: Fajstrup, Lisbeth
      foaf_surname: Fajstrup
  - foaf_Person:
      foaf_givenName: Morten M.
      foaf_name: Smedskjaer, Morten M.
      foaf_surname: Smedskjaer
  bibo_doi: 10.1038/s41467-026-77363-w
  bibo_volume: 17
  dct_date: 2026^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/2041-1723
  dct_language: eng
  dct_publisher: Springer Nature@
  dct_title: Predicting ionic conductivity of sulfide glass solid-state electrolytes
    using max-flow methodology@
  fabio_hasPubmedId: '42811056'
...