Writing the Programs of Programmable Catalysis

It has long been known that non-steady state and periodic catalytic reactor operation in temperature, pressure, and composition can lead to higher overall productivity or product selectivity than the best steady operation. Recently, the emergence of catalysts whose intrinsic properties can oscillate with time introduces novel forcing capabilities that can be "programmed" into the catalysts, so to broaden the scope and applicability of periodic operation to surface chemistry. In this work, an algorithmic approach was implemented to significantly accelerate the discovery and optimization of periodic steady states. Decomposition of complex dynamics into fundamental mechanistic fast-slow steps improves conceptual understanding of the relationship between binding energy oscillation protocols and overall catalytic rates. Finding the structured forcing protocols, optimally tailored to the multiple time scales of a given individual mechanism, requires efficient search of high-dimensional parameter spaces. This is enabled here through active learning (Bayesian Optimization enhanced by our proposed Bayesian Continuation). Implementation of these methods is shown to accelerate the evaluation of catalyst programs by up to several orders of magnitude. Faster screening of programmable catalysts to discover periodic steady states enables the optimization of catalytic operating protocols; it thus opens the possibility for catalyst engineering based on optimal forcing programs to control rate and product selectivity, even for complex multi-step catalytic mechanisms.