physics.chem-ph · 2026-09-20 · No. 119

Chemical Physics, 2026-09-20.

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

    Truncated automatic sparse differentiation for machine learning interatomic potentials

    Marcel F. Langer, Adrian Hill, Michele Ceriotti

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

    Machine learning interatomic potentials (MLIPs) learn the mapping from atomic positions to potential energy. The forces, the negative gradient of this energy, drive molecular dynamics and are readily obtained using automatic differentiation. Higher-order derivatives, most notably the Hessian, describe collective motion and allow the direct prediction of experimental observables, but are considered computationally inaccessible for large...

    arxiv.org/abs/2609.20510 · PDF

This edition is part of The Daily Abstract — physics.chem-ph archive. Subscribe to receive these in your inbox each morning, automatically translated to Spanish, with reply-to-PDF: arxivdaily.ignorelist.com.

Colophon Set in Georgia, with system sans for interface chrome and a monospaced stack for code and paper identifiers. Sole accent: amber #D99C5E. Built and served on an always-free VM. The masthead is set 14% letterspaced because newspapers do that and it works.