I-011

Pjotrs Žguns

pjotr.zgun@gmail.com

Konstantin Klyukin, Louis S. Wang, Grace Xiong, Ju Li, Sossina M. Haile and Bilge Yildiz

Institute of Solid State Physics, University of Latvia, Latvia

Uncovering fast solid-acid proton conductors using physical descriptors, high-throughput screening and machine-learning potentials

Achieving high proton conductivity in inorganic solids is key for advancing many electrochemical technologies, including low-power electronics, protonic fuel cells and electrolyzers. A quantitative understanding of the physical traits of a material that regulate proton diffusion is necessary for accelerating the discovery of fast proton conductors. In this work, we have mapped the structural, chemical and dynamic properties of solid acids to the elementary steps of the Grotthuss mechanism of proton diffusion. Our approach combines ab initio molecular dynamics simulations, analysis of phonon spectra and atomic structure calculations. We have identified the donor–hydrogen bond lengths and the acidity of polyanion groups as key descriptors of local proton transfer and the vibrational frequencies of the cation framework as the key descriptor of lattice flexibility. The latter facilitates rotations of polyanion groups and long-range proton migration in solid acid proton conductors. The calculated lattice flexibility also correlates with the experimentally reported superprotonic transition temperatures. Using these descriptors, we have screened the Materials Project database and identified potential solid acid proton conductors with monovalent, divalent and trivalent cations, including Ag+, Sr2+, Ba2+ and Er3+ cations, which go beyond the traditionally considered monovalent alkali cations (Cs+, Rb+, K+, and NH4+) in solid acids. By leveraging ab initio molecular dynamics simulations accelerated by machine learning potentials, we computationally evaluated the proton conductivity of the identified compounds, highlighting their potential. We conclude by discussing recent advancements in machine learning interatomic potentials and how they can accelerate the modeling and discovery of proton conductors.