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. 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.