{
  "abstract": "Designing sustainable alloys must reconcile high performance with real-world constraints, including feedstock scarcity and variability, processing and energy requirements, regulatory thresholds, and economic limitations. Addressing these constraints reaches far beyond today's conventional performance-focused frameworks. It needs to consider not only complex property-processing-performance trade-offs but also sustainable production pathways. Here, we propose a physics-informed ensemble (PIE) learning framework that integrates fundamental structure-property relationships with processing routes and sustainability criteria for mechanism-based alloy design. It predicts properties from composition-microstructure relationships and infers processing routes under sustainability constraints, establishing an interdisciplinary approach to identifying sustainable alloy solutions. We validate the approach for Mg alloys, a lightweight yet energy-intensive metallic material class, considering performance, cost, and carbon emissions. Trained on a benchmark dataset, the PIE model systematically explores plausible alloy compositions and microstructures to construct a digital library of millions of candidate alloys. From this space, we identify two sustainable Mg alloy solutions that overcome the conventional performance-cost-emission trade-off: a rare-earth-containing alloy increases yield and tensile strength by 72.8 percent and 61.1 percent, respectively, over alloys from the same family at comparable cost and emissions; and a rare-earth-free alloy reduces cost and carbon emissions by 40.4 percent and 88.9 percent, respectively, relative to its primary-feedstock counterpart, without compromising mechanical performance. This work introduces a general, physics-informed, and quantifiable framework for designing sustainable alloys under real-world constraints.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.35946",
  "author_total": 13,
  "authors": [
    {
      "name": "Quan Li"
    },
    {
      "name": "Zhilong Wang"
    },
    {
      "name": "Lei Shi"
    },
    {
      "name": "Baojing Feng"
    },
    {
      "name": "Changhui Liu"
    },
    {
      "name": "Jie Wang"
    },
    {
      "name": "Jingya Wang"
    },
    {
      "name": "Weisen Zheng"
    },
    {
      "name": "Matic JovicevicKlug"
    },
    {
      "name": "Ziyuan Rao"
    },
    {
      "name": "Jinjin Li"
    },
    {
      "name": "Dierk Raabe"
    },
    {
      "name": "Xiaoqin Zeng"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/98mce16cswpjk.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.35946",
  "fulltext_url": null,
  "id": "98mce16cswpjk",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.35946",
  "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.mtrl-sci"
    ],
    [
      "openalex",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7224137170",
  "pdf_url": "https://arxiv.org/pdf/2610.35946",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-23",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/98mce16cswpjk.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:35:50Z",
      "native_id": "2610.35946",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.35946"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.mtrl-sci",
      "subfield": "condensed-matter"
    }
  ],
  "title": "Machine learning enabled design of sustainable alloys considering real world constraints",
  "title_text": "Machine learning enabled design of sustainable alloys considering real world constraints",
  "type": "preprint",
  "updated_at": "2026-10-30T12:35:50Z",
  "url": "https://research.draconitic.com/paper/98mce16cswpjk",
  "venue": "arXiv",
  "versions": []
}