Electrocatalysis provides a practical solution to the increasing global energy demand while maintaining a sustainable environment. Recently nanoscale catalysts (nanoparticles, nanowires, and dealloyed surfaces) have been shown to have experimentally far superior performance than metallic crystals at sustainable energy conversion. However, the surface feature of these improved catalysts is still unknown, as the detection of the active sites directly from experiment has not been possible.
In this thesis work, we discuss using the quantum mechanics based muitiscale simulations and machine learning to understand the nature of these superior materials. We first studied jagged Pt nanowire (J-PtNW), which was shown to have performance at oxygen reduction reactions (ORR) 50 times better than Pt/C. We used multiscale simulations (reactive force field, and density functional theory) to explain this remarkably accelerated ORR activity from an atomistic perspective. Next, we looked into the irregular gold surfaces and copper surfaces (nanoparticles and dealloyed surfaces), which showed dramatically improved performance at CO2 reduction reactions (CO2RR) and CO reduction reactions (CORR). We developed the strategy to combine the reactive force field, density functional theory, and machine learning to identify the active sites responsible for their improved performance. This approach provided the possibility to understand the highly irregular and disordered surface, which is impossible with surface science experiments or with quantum mechanics. The identification of the active sites provides insights into new design concepts (alloys, NP, NW, and electrolytes such as ionic liquids) aimed at increasing product selectivity and rates simultaneously with reducing energy requirements.</p
