We report a mistake in the tables published in Milesi et al. (2019).
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| http://dx.doi.org/10.1007/s00204-020-02814-2 | DOI Listing |
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On machine learnability of local contributions to interatomic potentials from density functional theory calculations.
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Machine learning interatomic potentials, as a modern generation of classical force fields, take atomic environments as input and predict the corresponding atomic energies and forces. We challenge the commonly accepted assumption that the contribution of an atom can be learned from the short-range local environment of that atom. We employ density functional theory calculations to quantify the decay of the induced electron density and electrostatic potential in response to local perturbations throughout insulating, semiconducting and metallic samples of different dimensionalities.
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