| About this Abstract |
| Meeting |
MS&T23: Materials Science & Technology
|
| Symposium
|
Energy Materials for Sustainable Development
|
| Presentation Title |
Accelerated Investigation of Electrocatalysts with Integrated Computational Approaches |
| Author(s) |
Lingxiao Mu, Susan B. Sinnott |
| On-Site Speaker (Planned) |
Lingxiao Mu |
| Abstract Scope |
Pt based alloys have been widely used as electrocatalysts for surface reactions, ORRs (Oxygen Reduction Reactions) in fuel cells. To model the complexity of the interface, integrated computational approaches were leveraged in this work. One of the complicated Pt-based alloy surface structures is the corrugated Pt-skeleton structure, which forms after the 3d metals in Pt-M (M = Ni or Co) alloys dissolve into the electrolyte after voltage-cycling. Cluster expansion is an accurate and effective model for nanoalloys. However, it is computationally expensive to estimate the effective cluster interaction (ECI) parameters for a cluster expansion model for a low-symmetry system such as the Pt-skeleton structure. Τherefore, we used a Bayesian anaylsis approach with empirical interatomic potential priors to accelerate the estimation of ECIs. Our results indicate that this empirical priors can capture the energy-structure correlations, which leads to a faster sampling of the supercells, and an error-bounded estimates of the ECIs. |