{
  "abstract": "Accurate and efficient prediction of lattice thermal conductivity (LTC) is essential for developing materials for thermoelectric, microelectronic, and photovoltaic applications. Here, we present a methodology for high-throughput calculation of LTC across large datasets of compounds sharing a common crystal symmetry. Our approach combines fine-tuning of the MatterSim 1M universal machine-learned interatomic potential with LTC calculations based on the solution of the Boltzmann transport equation. We introduce a symmetry-based few-shot fine-tuning strategy (SBFS-FT) based on low-rank adaptation. In this approach, MatterSim 1M is fine-tuned on a small, carefully selected set of compounds that share a common crystal symmetry. The resulting potential is then applicable for accurate LTC calculations for compounds with this crystal symmetry, even if they were not included in the fine-tuning set. We tested our methodology on datasets of half-Heusler compounds (space group 216), materials with space group 225, and spinels (space group 227). We demonstrated that LTC calculations with MatterSim 1M fine-tuned using SBFS-FT exhibited a mean absolute relative difference from the reference results (obtained using moment tensor potentials and polyMLPs) 2-3 times smaller than that achieved with the non-fine-tuned MatterSim 1M potential. Moreover, the accuracy of LTC calculations with MatterSim 1M after SBFS-FT was comparable to that achieved using the MatterSim 1M potentials individually fine-tuned for each compound. Importantly, SBFS-FT enabled generation of the potential suitable for accurate LTC calculations across large compound datasets substantially faster than fine-tuning of MatterSim 1M for each compound individually, reducing the computational time by approximately a factor of 5-7 for a dataset of 100 compounds.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.29634",
  "author_total": 4,
  "authors": [
    {
      "name": "Rustam Arabov"
    },
    {
      "name": "Konstantin Sozykin"
    },
    {
      "name": "Alexander Shapeev"
    },
    {
      "name": "Nikita Rybin"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6j0swx455dv83.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.29634",
  "fulltext_url": null,
  "id": "6j0swx455dv83",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.29634",
  "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",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7223200193",
  "pdf_url": "https://arxiv.org/pdf/2610.29634",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-27",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/6j0swx455dv83.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:35:50Z",
      "native_id": "2610.29634",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.29634"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.mtrl-sci",
      "subfield": "condensed-matter"
    }
  ],
  "title": "Symmetry-Based Parameter Efficient Low-Rank Fine-Tuning for High-Throughput Calculation of Lattice Thermal Conductivity",
  "title_text": "Symmetry-Based Parameter Efficient Low-Rank Fine-Tuning for High-Throughput Calculation of Lattice Thermal Conductivity",
  "type": "preprint",
  "updated_at": "2026-10-30T12:35:50Z",
  "url": "https://research.draconitic.com/paper/6j0swx455dv83",
  "venue": "arXiv",
  "versions": []
}