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

Materials Science