(195g) Understanding Mixing Behavior of Bimetallic Nanoparticles through Genetic Algorithm Modeling

Bimetallic nanoparticles (NPs) have received tremendous interest due to their controllable properties that make them promising candidates for a wide range of applications, such as catalysis and electronics. Central to these applications is to predict the thermodynamic preference of metals mixing at the nanoscale as a function of NP size, shape and metal composition. However, a complexity in bimetallic NPs is the explosion in the size of configurational space. In fact, it becomes impossible to study the entire search space of bimetallic NPs as their size increases, especially if one relies on first-principles calculations. To combat this problem, we develop a genetic algorithm (GA) capable of targeted screening towards the discovery of stable bimetallic NPs of any given morphology. Our GA leverages a previously reported “Bond-Centric” cohesive energy model¹ as a fast and accurate stability metric with practically minimal computational cost. With this approach we achieve high-throughput screening and can effectively search the large configurational space of bimetallic NPs. Using results from our GA, we connect the morphology, composition, and chemical ordering to the mixing behavior of bimetallic NPs – focusing on combinations of Cu, Ag, and Au. Importantly, we incorporate entropic effects into our framework to determine how mixing behavior changes with temperature. Taken together, our results unravel the impact of NP morphology (size, shape), metal composition, and temperature to the thermodynamic mixing behavior of bimetallic NPs and elucidates their detailed structural architectures (i.e. positioning of different metals on different NP sites).

1. Yan, Z.; Taylor, M. G.; Mascareno, A.; Mpourmpakis, G., Size-, Shape-, and Composition-Dependent Model for Metal Nanoparticle Stability Prediction. Nano Lett. 2018, 18 (4), 2696-2704.