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 language | English |
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Pages (from-to) | 3581-3588 |
Number of pages | 8 |
Journal | Journal of Physical Chemistry Letters |
Volume | 14 |
Issue number | 14 |
DOIs | |
Publication status | Published - 13 Apr 2023 |
Austrian Fields of Science 2012
- 103018 Materials physics
- 102009 Computer simulation