High-Throughput Experimentation, Theoretical Modeling, and Human Intuition: Lessons Learned in Metal−Organic-Framework-Supported Catalyst Design

Abstract

We have screened an array of 23 metals deposited onto the metal−organic framework (MOF) NU-1000 for propyne dimerization to hexadienes. By a first-of-its-kind study utilizing data-driven algorithms and high-throughput experimentation (HTE) in MOF catalysis, yields on Cu-deposited NU-1000 were improved from 0.4 to 24.4%. Characterization of the best-performing catalysts reveal conversion to hexadiene to be due to the formation of large Cu nanoparticles, which is further supported by reaction mechanisms calculated with density functional theory (DFT). Our results demonstrate both the strengths and weaknesses of the HTE approach. As a strength, HTE excels at being able to find interesting and novel catalytic activity; any a priori theoretical approach would be hard-pressed to find success, as high-performing catalysts required highly specific operating conditions difficult to model theoretically, and initial simple single-atom models of the active site did not prove representative of the nanoparticle catalysts responsible for conversion to hexadiene. As a weakness, our results show how the HTE approach must be designed and monitored carefully to find success; in our initial campaign, only minor catalytic performances (up to 4.2% yield) were achieved, which were only improved following a complete overhaul of our HTE approach and questioning our initial assumptions.