(428d) Computational Design of Thermodynamically Stable Metal Nanoparticles

Mpourmpakis, G., University of Pittsburgh
The physicochemical properties of metal nanoparticles (e.g. catalytic behavior), as well as their thermodynamic stability, depend on the nanoparticle metal composition and morphological characteristics, such as their size and shape. For this reason, the application performance of metal nanoparticles is commonly a fine balance between their stability and optimal property functionality (e.g. catalytic activity). As a result, the computational design of nanoparticles that maximize their application properties should be performed utilizing criteria for the nanoparticle thermodynamic stability. In this work, we blend first-principles calculations with scientific computing to introduce a novel computational framework able to predict the stability of bimetallic nanoparticles of different morphology and metal composition. Our nanoparticle stability predictions are tested against experimentally synthesized nanostructures with excellent agreement.