Effect of framework composition and NH3 on the diffusion of Cu+ in Cu-CHA catalysts predicted by machine-learning accelerated molecular dynamics

TL;DR

This study uses machine-learning-accelerated molecular dynamics to explore how framework composition and ammonia influence Cu+ diffusion in Cu-CHA catalysts, revealing key factors that affect catalytic mobility and reactivity.

Contribution

The paper introduces a machine-learning interatomic potential enabling large-scale simulations of Cu-CHA, providing new atomistic insights into diffusion mechanisms influenced by framework composition and NH3.

Findings

Aluminum pairing accelerates local Cu+ hopping.

Increased NH3 enhances long-range Cu+ diffusion.

Higher Cu and Al content increases complex pairing probability.

Abstract

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.