physics.chem-ph · 2026-07-18 · No. 57

Chemical Physics, 2026-07-18.

1 new papers in physics.chem-ph. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

1 entries
  1. 01

    Full-data accuracy with fewer labels for training and fine-tuning machine-learning force fields

    Sheng Bi, Yi-Ze Wang, Jun Cheng

    physics.chem-ph · cond-mat.mtrl-sci · cs.LG

    Machine-learning force fields (MLFFs) are reliable only near their training distribution, making efficient construction of diverse training sets a major bottleneck for both train-from-scratch and foundation fine-tuning workflows. Active learning can reduce this cost, but standard model-committee uncertainty is impractical for foundation MLFFs because each committee member requires a separate fine-tuning run. We present an active-learning...

    arxiv.org/abs/2607.14486 · PDF

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