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Jeheon Woo

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  1. arXiv

    An AI agent rewrites ASE's numerical algorithms: six kernels 22–24 times faster, and optimizers needing 3.1–3.4 times fewer force evaluations on unseen problems

    AIOA places a large-language-model coding agent in a keep-or-discard loop that rewrites the numerical algorithms of the atomistic simulation library ASE, with verifiers the agent cannot alter guarding each keep. Across three independent runs, six numerical kernels ran 22–24 times faster on average in Python with NumPy and SciPy while returning unchanged results; on blind held-out problems, optimizers from the best configuration needed 3.1–3.4 times fewer force evaluations than L-BFGS for structure relaxation and 4.6–17 times fewer than standard CI-NEB for reaction paths.

    Jeheon Woo（First author, Corresponding author）
    AIOAAutonomous optimization of numerical algorithmsAtomistic simulationStructure relaxationClimbing-image nudged elastic band
  2. arXiv

    Electron densities skip the self-consistency loop: one Kohn-Sham step reproduces the self-consistent structure

    The electron density is the central variable of density functional theory, and obtaining it normally means iterating to self-consistency. Two Transformer-inspired networks, SwinUNETR-V2 and its attention-free counterpart NeXtUNETR, predict the density directly, reaching normalized mean absolute density errors of 0.075% and 0.046% on QM9. For NeXtUNETR, a single Kohn-Sham step on the predicted density reproduces the self-consistent electronic structure, with median errors of 0.036 meV in total energy, 0.65 meV/angstrom in forces and 1.8 meV in the HOMO-LUMO gap.

    Jeheon Woo（First author, Corresponding author）
    NeXtUNETRSwinUNETR-V2Electron Density PredictionDensity Functional TheoryTransformer

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