Unraveling Adsorbate-Induced Structural Evolution of Iron Carbide Nanoparticles

Iron carbide (FexCy) nanoparticles (NPs) are promising candidates for
replacing
platinum group metals in industrial applications, such as high-temperature
Fischer–Tropsch synthesis. However, due to their amorphous
nature, characterization of the active sites has been challenging
experimentally and computationally. Here, using a combined density
functional theory (DFT), neural network interatomic potential-assisted
global optimization, and ensemble learning study, we evaluate dynamic
surface changes associated with syngas (H and CO) interactions. For
this purpose, we have developed a general procedure that we use to
model an experimentally relevant 270-atom Fe182C88 NP using the neural network-assisted stochastic surface walk global
optimization algorithm (SSW-NN). Once generated, the Fe182C88 NP active sites and particle morphology are thoroughly
characterized before the effects of syngas adsorbate interactions
are explored by using DFT and molecular dynamics simulations. Lastly,
we explore correlations between geometric and electronic features
of the active sites and the adsorption of H (Hads), using
a regularized random forest machine learning algorithm. In doing so,
we identified the Fe–C coordination number and p orbital occupancy
as the most important descriptors affecting Hads. Using
a combined ML and quantum chemistry approach, our work demonstrates
a general and efficient procedure for generating and probing complex
surface phenomena on binary nanoparticles.