2023/05/22 by Reisel Millán, Estefanía Bello‐Jurado, Millan, Reisel +7
Chemistry · Materials Science · #Catalytic Processes in Materials Science #Chemical Physics (physics.chem-ph) #FOS: Physical sciences #Machine Learning in Materials Science #Metal-Organic Frameworks: Synthesis and Applications
paper · pdf · doi:10.48550/arxiv.2305.12896
openalex publication_date 2023/05/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Cu-exchanged zeolites rely on mobile solvated Cu+ cations for their catalytic activity, but the role of framework composition on transport is not fully understood. Ab initio molecular dynamics simulations can provide quantitative atomistic insight but are too computationally expensive to explore large length- and time-scales or diverse compositions. We report a machine-learning interatomic potential that accurately reproduces ab initio results and effectively generalizes to allow multi-nanosecond simulations of large supercells and diverse chemical compositions. Biased and unbiased simulations of [Cu(NH3)2]+ mobility show that aluminum pairing in eight-membered rings accelerates local hopping, and demonstrate that increased NH3 concentration enhances long-range diffusion. The probability of finding two [Cu(NH3)2]+ complexes in the same cage - key for SCR-NOx reaction - increases with Cu content and Al content, but does not correlate with the long-range mobility of Cu+. Supporting experimental evidence was obtained from reactivity tests of Cu-CHA catalysts with controlled chemical composition.