physics.chem-ph · 2026-07-27 · No. 66

Chemical Physics, 2026-07-27.

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

    Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems

    Xiao Zhu, Srinivasan S. Iyengar

    physics.chem-ph · cs.AI · physics.comp-ph

    Accurate ab initio molecular dynamics (AIMD) simulations of complex, fluxional chemical systems are severely limited by the high computational scaling of correlated electronic structure methods. To overcome this bottleneck, we present a robust, graph-theoretic molecular fragmentation framework integrated with machine learning to directly model post-Hartree-Fock nuclear forces at coupled cluster accuracy. Bypassing the limitations of automatic...

    arxiv.org/abs/2607.21779 · PDF

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