Catalyst design requires a detailed understanding of the structure of the catalyst surface as a function of varying reaction conditions. Here we demonstrate the capability of a grand canonical Monte Carlo/molecular dynamics (GC-MC/MD) method utilizing the ReaxFF potential to predict nanoparticle structure and phase stability as a function of temperature and pressure. This is demonstrated for Pd nanoparticles, which readily form oxide, hydride, and carbide phases under reaction environments, impacting catalytic behavior. The approach presented here can be extended to other catalytic systems, providing a new tool for exploring the effects of reaction conditions on catalyst activity, selectivity, and stability.
All Science Journal Classification (ASJC) codes
- Process Chemistry and Technology