Learning Design Rules for Selective Oxidation Catalysts from High-Throughput Experimentation and Artificial Intelligence

The design of heterogeneous
catalysts is challenged by the complexity
of materials and processes that govern reactivity and by the fact
that the number of good catalysts is very small in comparison to the
number of possible materials. Here, we show how the subgroup-discovery
(SGD) artificial-intelligence approach can be applied to an experimental
plus theoretical data set to identify constraints on key physicochemical
parameters, the so-called SG rules, which exclusively
describe materials and reaction conditions with outstanding catalytic
performance. By using high-throughput experimentation, 120 SiO2-supported catalysts containing ruthenium, tungsten, and phosphorus
were synthesized and tested in the catalytic oxidation of propylene.
As candidate descriptive parameters, the temperature and 10 parameters
related to the composition and chemical nature of the catalyst materials,
derived from calculated free-atom properties, were offered. The temperature,
the phosphorus content, and the composition-weighted electronegativity
are identified as key parameters describing high yields toward the
value-added oxygenate products acrolein and acrylic acid. The SG rules
not only reflect the underlying processes particularly associated
with high performance but also guide the design of more complex catalysts
containing up to five elements in their composition.