  The efficiency of atomic simulations of materials and molecules can rapidly
deteriorate when large free energy barriers exist between local minima. We
propose smooth basin classification, a universal method to define reaction
coordinates based on the internal feature representation of a graph neural
network. We achieve high data efficiency by exploiting their built-in symmetry
and adopting a transfer learning strategy. We benchmark our approach on
challenging chemical and physical transformations, and show that it matches and
even outperforms reaction coordinates defined based on human intuition.
