DPA4 is a new SE(3)-equivariant interatomic potential with EMFA SO(2) convolution that sets new accuracy-cost records on Matbench Discovery and SPICE benchmarks using fewer parameters than prior models.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
A λ-convex variational surrogate for shallow NN training yields global well-posedness, almost C³ regularity, and an explicit linear-system solution with 1/α generalization and O(1/N) finite-width rates.
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DPA4: Pushing the Accuracy-Cost Frontier of Interatomic Potentials with EMFA SO(2) Convolution
DPA4 is a new SE(3)-equivariant interatomic potential with EMFA SO(2) convolution that sets new accuracy-cost records on Matbench Discovery and SPICE benchmarks using fewer parameters than prior models.
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Born Discrete, Made Smooth: Variational Formulation of Shallow Neural Networks
A λ-convex variational surrogate for shallow NN training yields global well-posedness, almost C³ regularity, and an explicit linear-system solution with 1/α generalization and O(1/N) finite-width rates.