{
  "abstract": "Machine-learning models that relate local atomic structure to the thermodynamic state of metallic glasses typically assess physical consistency after training rather than enforcing it during learning. Here, we develop a multi-task physics-informed neural network (PINN) that predicts temperature directly from Voronoi-motif population histograms while incorporating autograd-derived gradient constraints representing physically motivated relationships between structural motifs, quench rate, and temperature. The model simultaneously classifies each configuration as liquid, supercooled/transition, or glass through an auxiliary classification head. The classification task achieves 96.6% test accuracy with a macro-F1 score of 0.95, while the regression head yields a mean absolute error of 23.4 K on a trajectory-disjoint held-out test set. Five-fold trajectory-grouped cross-validation, deep-ensemble predictions, and Monte Carlo dropout are used to assess model robustness and predictive uncertainty. Sensitivity analyses demonstrate that physics constraints can be incorporated over a broad range of loss weights without compromising predictive accuracy, while substantially improving compliance with the prescribed physical trends. Benchmarking against conventional machine-learning regressors further demonstrates competitive predictive performance. SHAP analysis across the cross-validation ensemble identifies the coupled near-icosahedral motif family, particularly the full icosahedral motif and its single-atom-perturbed counterpart, as the dominant structural fingerprints of the glassy state. These results demonstrate that physically motivated constraints can be embedded directly into motif-based structure-property models, providing a pathway from post-hoc interpretability toward physically constrained machine learning for metallic glasses.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.22992",
  "author_total": 1,
  "authors": [
    {
      "name": "Prashil S. Joshi"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/91e38f2rgaew8.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.22992",
  "fulltext_url": null,
  "id": "91e38f2rgaew8",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.22992",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cond-mat.mtrl-sci"
    ],
    [
      "openalex",
      "2210|Metallic Glasses and Amorphous Alloys"
    ]
  ],
  "openalex": "W7214048292",
  "pdf_url": "https://arxiv.org/pdf/2609.22992",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-19",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/91e38f2rgaew8.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:23:19Z",
      "native_id": "2609.22992",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.22992"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.mtrl-sci",
      "subfield": "condensed-matter"
    }
  ],
  "title": "From Local Atomic Motifs to Thermodynamic State: An Interpretable Physics-Informed Framework for Cu-Zr Metallic Glasses",
  "title_text": "From Local Atomic Motifs to Thermodynamic State: An Interpretable Physics-Informed Framework for Cu-Zr Metallic Glasses",
  "type": "preprint",
  "updated_at": "2026-10-30T12:23:19Z",
  "url": "https://research.draconitic.com/paper/91e38f2rgaew8",
  "venue": "arXiv",
  "versions": []
}