Machine learning-driven molecular dynamics unveils a bulk phase transformation driving ammonia synthesis on barium hydride.

ABSTRACT: The modern view of industrial heterogeneous catalysis is evolving from the traditional static paradigm where the catalyst merely provides active sites, to that of a functional material in which dynamics plays a crucial role. Using machine learning-driven molecular dynamics simulations, we confirm this picture for the ammonia synthesis catalysed by BaH₂. Recent experiments show that this system acts as a highly efficient catalyst, but only when exposed first to N₂ and then to H₂ in a chemical looping process. Our simulations reveal that when first exposed to N₂, BaH₂ undergoes a profound change, transforming into a superionic mixed compound, BaH_2-2x(NH)_x, characterized by a high mobility of both hydrides and imides

SUBMITTER: Tosello Gardini A 

PROVIDER: S-EPMC11903671 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature