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

We have screened an array of 23 metals deposited onto the metal–organic framework (MOF) NU-1000 for propyne dimerization to hexadienes under different reaction conditions for a total of ~1400 experiments. 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 4.2% to 24.4%. Characterization of the most-performant 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 naïve 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 (~six months and over half of the total experiments conducted) only minor catalytic performances (up to 4.2% yield) were achieved, which was only improved following a complete overhaul of our HTE approach and questioning our initial assumptions. Thus, the HTE approach is much less automated than it may seem, even if driven by machine learning algorithms – one must carefully design their HTE campaign to find success.