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

Autor(en)

Ryosuke Jinnouchi, Saori Minami, Ferenc Karsai, Carla Verdi, Georg Kresse

Abstrakt

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(en)

Computergestützte Materialphysik

Externe Organisation(en)

VASP Software GmbH, Toyota Central R&D Labs., Inc.

Journal

Journal of Physical Chemistry Letters

Band

14

Seiten

3581-3588

Anzahl der Seiten

8

ISSN

1948-7185

DOI

https://doi.org/10.1021/acs.jpclett.3c00293

Publikationsdatum

04-2023

Peer-reviewed

Ja

ÖFOS 2012

103018 Materialphysik, 102009 Computersimulation

ASJC Scopus Sachgebiete

Allgemeine Materialwissenschaften, Physical and Theoretical Chemistry

Link zum Portal

https://ucrisportal.univie.ac.at/de/publications/a8e15aec-10c2-47ec-b590-393396a0ceb0