cond-mat.mtrl-sci · 2026-08-20 · No. 90

Materials Science, 2026-08-20.

1 new papers in cond-mat.mtrl-sci. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

1 entries
  1. 01

    A single design choice determines whether machine learning models of materials make physically impossible predictions

    Can Polat, Mustafa Kurban, Erchin Serpedin, Hasan Kurban

    cond-mat.mtrl-sci · cs.LG · physics.comp-ph · quant-ph

    Machine-learned models are replacing first-principles calculations across materials discovery, and physical symmetry is the central guarantee built into them. The debate over how much symmetry to hard-wire rather than learn has run on rotations, where a symmetry error is an approximation error. Some constraints are exact: symmetry forces certain property tensors to exactly zero, so a nonzero prediction is physically impossible rather than...

    arxiv.org/abs/2608.18714 · PDF

This edition is part of The Daily Abstract — cond-mat.mtrl-sci 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.