Multicomponent metal alloys have long been a central focus of computational catalyst screening research, and in recent years, exciting progress has been made in both the complexity of alloy structures and the extent of reaction mechanisms that may be treated with these techniques.  In this talk, we present two brief examples of these trends.  First, we describe a surface science-motivated study in which an extensive reaction network is computationally analyzed for the non-oxidative reaction of propane, from which novel descriptors of the selectivity and activity of this chemistry are derived.  A machine learning-inspired strategy is then used to screen a set of several hundred intermetallic alloys for PDH, leading to the identification of several promising new catalyst candidates.  Second, we discuss how these and related techniques may be extended to systems wherein the configurational space of accessible alloys, including high entropy alloy systems with four or more distinct elements per alloy, is on the order of millions of potential configurations and compositions.  We describe strategies for the rapid evaluation of the structural and catalytic features of such alloys, and we present preliminary screening results identifying stable and catalytically active alloys for nitrogen-based chemistries.

Catalyst screening on intermetallic/high entropy alloys: examples of propane dehydrogenation and ammonia decomposition

ACS Spring 2022

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