Benchmarking Computational Alchemy for Carbide, Nitride, and Oxide Catalysts

Kohn–Sham density functional theory (DFT)‐based searches for hypothetical catalysts are too computationally demanding for wide searches through diverse materials space. Here, the accuracy of computational alchemy schemes on carbides, nitrides, and oxides is assessed. With a single set of reference DFT calculations, computational alchemy approximates adsorbate binding energies (BEs) on a large number of hypothetical catalysts surfaces with negligible computational cost. Analogous to previous studies on metal alloys, computational alchemy predicts adsorbate BEs on rocksalt TiC(111), TiN(100), and TiO(100) materials, which have no bandgap, in close agreement with DFT results (with mean unsigned errors up to 0.33 eV). In contrast, it is found that semiconducting systems such as rutile TiO2(110), rutile SnO2(110), and rocksalt ZnO(100) can present more significant challenges. This work identifies these challenges being linked to the density of states at the Fermi level and by adding Pt dopants in the surface layer of TiO2, it is shown that computational alchemy can become more reliable with non‐transition metal systems. This remedy provides insight that promotes computational alchemy for broad searches for catalyst active sites through materials space beyond transition metal alloys. The applicability of computational alchemy for screening catalyst sites is validated for model carbide, nitride, and oxide systems. Materials having significant band gaps are prone for errors. Metal doping to add states to the Fermi level ameliorate these problems and make alchemy promising for high‐throughput screening of catalyst sites.