Atomistic Insights into H2O2 Direct Synthesis of Ni–Pt Nanoparticle Catalysts under Water Solvents by Reactive Molecular Dynamics Simulations

In computational catalysis, density-functional theory (DFT) calculations are usually utilized, although they suffer from high computational costs. Thus, it would be challenging to explicitly predict the catalytic properties of nanoparticles (NPs) at the nanoscale under solvents. Using molecular dynamics (MD) simulations with a reactive force field (ReaxFF), we investigated the catalytic performance of Ni–Pt NPs for the direct synthesis of hydrogen peroxide (H 2 O 2 ), in which water solvents were explicitly considered along with the effects of the sizes (1.5, 2.0, 3.0, and 3.5 nm) and compositions (Ni 90 Pt 10, Ni 80 Pt 20, and Ni 50 Pt 50 ) of the NPs. Among the Ni–Pt NPs, 3.0 nm NPs show the highest activity and selectivity for the direct synthesis of H 2 O 2, revealing that the catalytic performance is not well correlated with the surface areas of NPs. The superior catalytic performance results from the high H 2 dissociation and low O 2 dissociation properties, which are correlated with the numbers of NiNiPt-fcc and NiNi-bridge sites on the surface of Ni–Pt NPs, respectively. The ReaxFF-MD simulations propose the optimum composition (Ni 80 Pt 20 ) of 3.0 nm Ni–Pt NPs, which is also explained by the numbers of NiNiPt-fcc and NiNi-bridge sites. Furthermore, from the ReaxFF-MD simulations, the direct synthesis of H 2 O 2 for the Ni–Pt NPs can be achieved not only with the Langmuir–Hinshelwood mechanism, which has been conventionally considered, but also with the water-induced mechanism, which is unlikely to occur on pure Pd and Pd-based alloy catalysts; these results are supported by DFT calculations. These results reveal that the ReaxFF-MD method provides significant information for predicting the catalytic properties of NPs, which could be difficult to provide with DFT calculations; thus, it can be a useful framework for the design of nanocatalysts through complementation with a DFT method.