Dresden 2026 – scientific programme

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FM: Fachverband Funktionsmaterialien

FM 11: Poster Session Functional Materials

FM 11.13: Poster

Tuesday, March 10, 2026, 18:00–20:30, P4

Efficient Discovery of Intermediate-Temperature Proton Conductors via Ion Exchange Strategy with Machine Learning Potential — •Yun An, Karsten Reuter, and Christoph Scheurer — Fritz Haber Institute of the Max Planck Society, 14195 Berlin, Germany

Proton-conducting solid-oxide fuel cells (H-SOFCs) are promising for direct ammonia-fed SOFC applications, as they eliminate NO_x emissions compared to conventional oxide-ion-conducting solid-oxide fuel cells. However, current proton-conducting electrolytes operate at high temperatures and suffer from leakage currents. Known suitable materials remain rare, calling for systematic investigations into novel proton conductor classes. We present a framework that combines machine-learning techniques with high-throughput computation to screen and design efficient proton conductors at intermediate temperatures. Through an ion-exchange strategy applied to known alkaline-ion conductor classes, we identify a database of potential proton-conducting materials. Machine learning molecular dynamics simulations are employed to assess stability and diffusion barriers, enabling the selection of top-performing candidates. The screened proton conductors contain cations that span from monovalent to pentavalent, including Rb⁺, Ba²⁺, Ca²⁺, B³⁺, Zr⁴⁺, and Nb⁵⁺, and predominantly feature SO₄²⁻ and PO₄³⁻ polyanion groups. Proton transport in these materials follows the Grotthuss mechanism. The strength of hydrogen bonds increases after ion-exchange engineering, thereby enhancing proton transfer.

Keywords: Intermediate-temperature proton conductor; Solid oxide fuel cells; Ion exchange engineering; Machine learning potential