  We propose a new technique for classifying 5d Superconformal Field Theories
arising from brane webs in Type IIB String Theory, using technology from
Machine Learning to identify different webs giving rise to the same theory. We
concentrate on webs with three external legs, for which the problem is
analogous to that of classifying sets of 7-branes. Training a Siamese Neural
Network to determine equivalence between any two brane webs shows an improved
performance when webs are considered equivalent under a weaker set of
conditions. Thus, Machine Learning teaches us that the conjectured
classification of 7-brane sets is not complete, which we confirm with explicit
examples.
