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Gaussian Approximation Potentials: the accuracy of quantum mechanics, without the electrons

1 Pith paper cite this work. Polarity classification is still indexing.

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abstract

We introduce a class of interatomic potential models that can be automatically generated from data consisting of the energies and forces experienced by atoms, derived from quantum mechanical calculations. The resulting model does not have a fixed functional form and hence is capable of modeling complex potential energy landscapes. It is systematically improvable with more data. We apply the method to bulk carbon, silicon and germanium and test it by calculating properties of the crystals at high temperatures. Using the interatomic potential to generate the long molecular dynamics trajectories required for such calculations saves orders of magnitude in computational cost.

years

2026 1

verdicts

UNVERDICTED 1

representative citing papers

Polaron Transport in TiO$_{2}$ from Machine Learning Molecular Dynamics

cond-mat.mtrl-sci · 2026-06-01 · unverdicted · novelty 7.0

DeepPolaron ML-MD simulations show rutile electrons form Ti-localized polarons hopping along [001] with 39 meV barrier and 4.4e-2 cm2/Vs mobility, while anatase holes form O-localized polarons hopping to second neighbors with 139 meV barrier and 1.4e-3 cm2/Vs mobility.

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  • Polaron Transport in TiO$_{2}$ from Machine Learning Molecular Dynamics cond-mat.mtrl-sci · 2026-06-01 · unverdicted · none · ref 74 · internal anchor

    DeepPolaron ML-MD simulations show rutile electrons form Ti-localized polarons hopping along [001] with 39 meV barrier and 4.4e-2 cm2/Vs mobility, while anatase holes form O-localized polarons hopping to second neighbors with 139 meV barrier and 1.4e-3 cm2/Vs mobility.