PNNL

Abstract

Iron carbide (FexCy) nanoparticles (NPs) are promising candidates for replacing platinum group metals in industrial applications, such as the 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 novel procedure 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 using DFT and molecular dynamics (MD) 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 (RRF) machine learning algorithm. In doing so, we have identified the Fe-C coordination number and p-orbital filling 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.