Proton Transport in Perfluorinated Ionomer Simulated by Machine-Learned Interatomic Potential

Ryosuke Jinnouchi (Corresponding author), Saori Minami, Ferenc Karsai, Carla Verdi, Georg Kresse

Publications: Contribution to journalArticlePeer Reviewed

Abstract

Polymers are a class of materials that are highly challenging to deal with using first-principles methods. Here, we present an application of machine-learned interatomic potentials to predict structural and dynamical properties of dry and hydrated perfluorinated ionomers. An improved active-learning algorithm using a small number of descriptors allows to efficiently construct an accurate and transferable model for this multielemental amorphous polymer. Molecular dynamics simulations accelerated by the machine-learned potentials accurately reproduce the heterogeneous hydrophilic and hydrophobic domains formed in this material as well as proton and water diffusion coefficients under a variety of humidity conditions. Our results reveal pronounced contributions of Grotthuss chains consisting of two to three water molecules to the high proton mobility under strongly humidified conditions.
Original languageEnglish
Pages (from-to)3581-3588
Number of pages8
JournalJournal of Physical Chemistry Letters
Volume14
Issue number14
DOIs
Publication statusPublished - 13 Apr 2023

Austrian Fields of Science 2012

  • 103018 Materials physics
  • 102009 Computer simulation

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