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Symmetry- and Property-Aware Crystal Generation with Reinforcement Learning for Inverse Materials Design — arXiv:2609.13468

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.13468
License (as archived): CC0-1.0
Archived: 2026-10-15T22:48:29.319Z
SHA-256 of the archived text: d021b1ff40cd127c24212b8a921d3b6190c2601a287f6955a2eb24d2e269c0df
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How to cite

Symmetry- and Property-Aware Crystal Generation with Reinforcement Learning for Inverse Materials Design — arXiv:2609.13468 — arXiv (en), https://arxiv.org/abs/2609.13468 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ef938bdcd146 (Metraipolis archive dc_ef938bdcd146, sha256 d021b1ff40cd127c24212b8a921d3b6190c2601a287f6955a2eb24d2e269c0df)

BibTeX

@misc{dcef938bdcd146,
  title = {Symmetry- and Property-Aware Crystal Generation with Reinforcement Learning for Inverse Materials Design — arXiv:2609.13468},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.13468},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ef938bdcd146; sha256 d021b1ff40cd127c24212b8a921d3b6190c2601a287f6955a2eb24d2e269c0df},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.13468. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

The inverse design of crystalline materials ultimately seeks structures with desired physical properties. However, for many functional responses, a favorable numerical value is meaningful only when supported by the symmetry of the underlying crystal. Without the appropriate crystallographic constraints, an apparent response may be ill defined, accidental, or not symmetry protected. Here we introduce SPARC, a symmetry- and property-aware reinforcement learning framework that optimizes physical objectives while preserving the structural conditions required for their realization. We demonstrate SPARC on two complementary tasks. The first targets strong uniaxial dielectric anisotropy, a tensorial response that is well defined only within appropriate crystal classes. The second maximizes the spectroscopic limited maximum efficiency, a scalar device-level objective without a prescribed symmetry class, allowing the framework to identify favorable crystallographic motifs. These results show that symmetry is not merely an additional design constraint, but a physical foundation for generating candidates with meaningful, robust, and realizable functional properties.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-15 in cs.AI).
Categorie arXiv: Materials Science (cond-mat.mtrl-sci) ; Artificial Intelligence (cs.AI); Computational Physics (physics.comp-ph); Optics (physics.optics).
Pagina del lavoro: https://arxiv.org/abs/2609.13468 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-15T22:29:03.485Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-09-15?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).