Volcanos, cliffs and ways around: strategies to break linear scaling relationships in Catalysis

The search for new catalytic materials has relied in highly time consuming procedures. The appearance of theoretical methods that employ Density Functional Theory coupled to kinetic models has allowed the rational understanding of activity volcano plots and selectivity abrupt profiles that resemble cliffs. However, these methodologies present several drawbacks as the optimization is confined to a family of materials and the maximum activity might not be enough for practical applications. Volcanos emerge from the symmetry between the adsorption energies of different intermediates on the catalyst and thus circumventing these dependencies is crucial to identify better catalytic materials. I present a revision of the advances in the field indicating that complexity in the materials is crucial to allow alternative paths and thus overcome the drawbacks of scaling-relationships and how artificial intelligence techniques will enhance rational catalytic design.