Tensor-train formulation reduces multidimensional inverse Laplace transform cost from exponential to polynomial under low-rank assumptions.
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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.
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A tensor-train multidimensional inverse Laplace transform
Tensor-train formulation reduces multidimensional inverse Laplace transform cost from exponential to polynomial under low-rank assumptions.
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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.