{
  "abstract": "As general-purpose Large Language Models (LLMs), such as Gemini and DeepSeek, exhibit increasingly advanced reasoning capabilities, a critical question arises: how do their property prediction abilities compare to those of domain-specific pretrained materials models? In this study, we evaluate predictions for Ti, steels, high-entropy alloy (HEA) and Al, demonstrating that general-purpose LLMs surprisingly outperform SteelBERT, MatSciBERT, and SciBERT. To further enhance performance, we introduce a hybrid approach that utilizes numerical priors from a prior model, such as CatBoost or SteelBERT, to guide the general LLMs (Deepseek V3.1, Gemini-2.5-Flash). By inputting these initial estimates from a prior model as prompts, the LLM acts as a reasoning corrector, achieving accuracy superior to the prior model and \"unguided mode\". Furthermore, we investigate the impact of data strategy using a similarity-based metric, revealing that data quality dominates quantity; using merely 5% of high-similarity reference data of training set as input yields performance similar to the results of using 100% of training set. Additionally, the LLMs demonstrate excellent uncertainty calibration, where self-reported \"high\" confidence strongly correlates with minimal deviation from ground truth. Our findings suggest that synergizing general-purpose LLMs with prior model numerical baselines and high-quality data curation creates a cost-effective and accurate paradigm for materials discovery, reducing the need for expensive domain-specific pretraining.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2611.03767",
  "author_total": 6,
  "authors": [
    {
      "name": "YuJie Liu"
    },
    {
      "name": "Dezhen Xue"
    },
    {
      "name": "Jun Sun"
    },
    {
      "name": "Xiangdong Ding"
    },
    {
      "name": "Zhibin Gao"
    },
    {
      "name": "Turab Lookman"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/61pkrr1kjqfmk.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.03767",
  "fulltext_url": null,
  "id": "61pkrr1kjqfmk",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.03767",
  "language": null,
  "licence": "CC-BY-NC-SA-4.0",
  "licence_label": "CC BY-NC-SA 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-nc-sa/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cond-mat.mtrl-sci"
    ],
    [
      "arxiv",
      "cs.LG"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2611.03767",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-02",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/61pkrr1kjqfmk.ris",
  "sources": [
    {
      "first_seen": "2026-11-04T15:21:25Z",
      "native_id": "2611.03767",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.03767"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.mtrl-sci",
      "subfield": "condensed-matter"
    }
  ],
  "title": "Quantifying out-of-distribution data severity with Wasserstein distances: benchmarking large language models for alloy property prediction",
  "title_text": "Quantifying out-of-distribution data severity with Wasserstein distances: benchmarking large language models for alloy property prediction",
  "type": "preprint",
  "updated_at": "2026-11-06T18:21:01Z",
  "url": "https://research.draconitic.com/paper/61pkrr1kjqfmk",
  "venue": "arXiv",
  "versions": []
}