  Heterogeneous catalysis is an example of a complex materials function,
governed by an intricate interplay of several processes, e.g., the different
surface chemical reactions, and the dynamic re-structuring of the catalyst
material at reaction conditions. Modelling the full catalytic progression via
first-principles statistical mechanics is impractical, if not impossible.
Instead, we show here how a tailored artificial-intelligence approach can be
applied, even to a small number of materials, to model catalysis and determine
the key descriptive parameters ("materials genes") reflecting the processes
that trigger, facilitate, or hinder catalyst performance. We start from a
consistent experimental set of "clean data", containing nine vanadium-based
oxidation catalysts. These materials were synthesized, fully characterized, and
tested according to standardized protocols. By applying the symbolic-regression
SISSO approach, we identify correlations between the few most relevant
materials properties and their reactivity. This approach highlights the
underlying physicochemical processes, and accelerates catalyst design.
