Chemical production is the single largest consumer of energy in US manufacturing, according to the 2015 DOE Bandwidth Report; but the development of efficient catalysts for many processes continues to elude catalyst scientists. The basis of this project is the realization that a catalytic interface in the steady state is in constant motion enabled by the reaction conditions (temperature and pressure of gases in thermal catalysis, or electrochemical potential, solvent and pH in electrocatalysis). Due to these dynamics, the interface presents a fluxional ensemble of many states and active sites (rather than just one), each characterized by its specific activity, selectivity, deactivation propensity, and operando spectral signatures. Catalysis, therefore, is a collective ensemble phenomenon, largely driven by highly active metastable states rather than the ground state.

Operating within this new paradigm, this project addresses the nature of the catalytic interface in reaction conditions, attainable swarms of mechanistic pathways, and routes of deactivation, for size-selected fluxional cluster catalysts deposited on supporting surfaces. Predictions toward improved activities, selectivity, and stabilities will be made and experimentally tested. Additionally, the researchers will probe several fundamental phenomena that are expected to shift the new paradigm even further: sintering of clusters on amorphous surfaces in the presence of adsorbates that is expected to be strongly driven by metastable states, interpretations of operando spectra in view of the apparently ensemble-averaged nature of the experimental signal, and broken scaling relations that point toward better cluster catalysts in counterintuitive ways. Machine learning tools will be developed to replace costly DFT calculations wherever possible.