Big data and artificial intelligence has revolutionized science in almost every field – from economics to physics. In the area of materials science and computational heterogeneous catalysis, this revolution has led to the development of scientific data repositories, as well as data mining and machine learning tools to investigate the vast materials space. The goal of using these tools is to establish a deeper understanding of the relations between materials properties and activity, selectivity and stability – the important figures of merit in catalysis. Based on these insights, catalyst design principles can be established, which hopefully lead us to discover highly efficient catalysts to solve pressing issues for a sustainable future and the synthesis of highly functional materials, chemicals and pharmaceuticals. The inherent complexity of catalytic reactions quests for machine learning methods to efficiently navigate through the high‐dimensional hyper‐surfaces in structure optimization problems to determine relevant chemical structures and transition states. In this review, we show how cutting edge data infrastructures and machine learning methods are being used to address problems in computational heterogeneous catalysis.

Polycrystalline anatase thin films, (001)- and (101)-oriented anatase TiO ${}_2$ single crystals and (001)- and (110)-oriented rutile TiO ${}_2$ single crystals with various surface treatments were studied by photoelectron spectroscopy to obtain their surface potentials. Regardless of orientations and polymorph, a huge variation of the Fermi level and work function was achieved by varying the surface condition. The most strongly oxidized surfaces are obtained after oxygen plasma treatment with a Fermi level ∼2.6 eV above the valence band maximum and ionization potentials of up to 9.5 eV (work function 7.9 eV). All other treated anatase surfaces exhibit an ionization potential independent of surface condition of $7.96\pm 0.15$ eV. The Fermi level positions and the work functions vary by up to 1 eV. The ionization potential of rutile is ∼0.56 eV lower than that of anatase in good agreement with recent band alignment studies.