NEPMaker uses D-optimality active learning to identify and locally embed extrapolative atomic environments from large simulations into periodic structures for training neuroevolution potentials, aiming to cut extrapolation errors in complex materials.
Brillouin zone sampling was carried out using a Γ-centered k-point grid with a spacing less than 0.5 Å⁻¹
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NEPMaker: Active learning of neuroevolution machine learning potential for large cells
NEPMaker uses D-optimality active learning to identify and locally embed extrapolative atomic environments from large simulations into periodic structures for training neuroevolution potentials, aiming to cut extrapolation errors in complex materials.