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Generic neural network potentials without forcing users to train potentialscould result in significantly acceleration of total energy calculations.Takamoto et al. [Nat. Commun. (2022), 13, 2991] developed such a deep neuralnetwork potential (NNP) and made it available in their Matlantis package. Wecompared the Matlantis bulk formation, surface, and surface O vacancy formationenergies of metal hydrides, carbides, nitrides, oxides, and sulfides with ourpreviously calculated VASP values obtained from first-principles with thePBEsol(+U) functional. Matlantis bulk formation energies were consistently ~0.1eV/atom larger and the surface energies were typically ~10 meV/{\AA}^2 smallerthan the VASP counterpart. Surface O vacancy formation energies were generallyunderestimated within ~0.8 eV. These results suggest that Matlantis energiescould serve as a relatively good descriptor of the VASP bulk formation andsurface energies.

Comparison of Matlantis and VASP bulk formation and surface energies in metal hydrides, carbides, nitrides, oxides, and sulfides