{
  "abstract": "A machine learning (ML) framework for phase-diagram construction in condensed matter systems with unknown phase structure is presented. The method combines physically motivated descriptors with contrastive representation learning and a two-stage data-generation workflow. First, a sparse set of configurations is generated by Monte Carlo simulated annealing in order to train a contrastive neural network that produces a low-dimensional embedding. Consequently, representative phase configurations are used as initial conditions for gradient-based optimization on a dense parameter grid, enabling efficient generation of refined datasets for high-resolution phase-diagram construction. In order to optimize the workflow, a variant of the method, in which derived descriptors are used as soft labels in the contrastive objective, is examined as well. The framework is demonstrated on a PbZrO3-inspired model potential formulated in terms of coupled order-parameter fields and used as a physically motivated testbed for exploring configuration diversity and phase behavior. The proposed approach provides a physically informed route to ML-assisted phase-diagram construction that integrates data analysis with adaptive dataset refinement.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.08116",
  "author_total": 4,
  "authors": [
    {
      "name": "Vít Beneš"
    },
    {
      "name": "Pavel Baláž"
    },
    {
      "name": "Dmytro Bohdanov"
    },
    {
      "name": "Jiří Hlinka"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3ysqxbpwnqye4.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.08116",
  "fulltext_url": null,
  "id": "3ysqxbpwnqye4",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.08116",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cond-mat.dis-nn"
    ],
    [
      "openalex",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7220912320",
  "pdf_url": "https://arxiv.org/pdf/2610.08116",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-06",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/3ysqxbpwnqye4.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:22Z",
      "native_id": "2610.08116",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.08116"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.dis-nn",
      "subfield": "condensed-matter"
    }
  ],
  "title": "Soft Contrastive Learning for Unsupervised Discovery of Phases of Matter",
  "title_text": "Soft Contrastive Learning for Unsupervised Discovery of Phases of Matter",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:22Z",
  "url": "https://research.draconitic.com/paper/3ysqxbpwnqye4",
  "venue": "arXiv",
  "versions": []
}