Abstract

Abstract Generic neural network potentials without forcing users to train potentials could result in significant 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. Matlantis bulk formation energies of metal hydrides, carbides, nitrides, oxides, and sulfides were consistently ∼0.1 eV/atom larger and the surface energies were typically ∼10 meV/Å2 smaller than our previously calculated PBEsol(+U) VASP energies.

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