Proton Transport in Perfluorinated Ionomer Simulated by Machine-Learned Interatomic Potential
- Author(s)
- Ryosuke Jinnouchi, Saori Minami, Ferenc Karsai, Carla Verdi, Georg Kresse
- 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.
- Organisation(s)
- Computational Materials Physics
- External organisation(s)
- VASP Software GmbH, Toyota Central R&D Labs., Inc.
- Journal
- Journal of Physical Chemistry Letters
- Volume
- 14
- Pages
- 3581-3588
- No. of pages
- 8
- ISSN
- 1948-7185
- DOI
- https://doi.org/10.1021/acs.jpclett.3c00293
- Publication date
- 04-2023
- Peer reviewed
- Yes
- Austrian Fields of Science 2012
- 103018 Materials physics, 102009 Computer simulation
- ASJC Scopus subject areas
- Materials Science(all), Physical and Theoretical Chemistry
- Portal url
- https://ucris.univie.ac.at/portal/en/publications/proton-transport-in-perfluorinated-ionomer-simulated-by-machinelearned-interatomic-potential(a8e15aec-10c2-47ec-b590-393396a0ceb0).html