{
  "abstract": "The convergence of large language models, materials-specific foundation models, and agentic artificial intelligence is reshaping the paradigm of computational materials discovery. While high-throughput computation, automated workflows, and data-driven modeling have greatly expanded the scale of materials exploration, the core scientific decision-making loop remains largely human-directed. Agentic AI introduces the possibility of systems that can autonomously reason about materials objectives, execute simulations, and refine strategies. However, the rapid emergence of such systems has created a critical need for a unified and operational framework to define, evaluate, and guide scientific autonomy in computational materials discovery. In this Perspective, we propose the Computational Materials Agent Autonomy Level (CMA-AL) framework, a hierarchical taxonomy defining six levels of autonomous agency in computational materials science: scripted excecutor, LLM-assisted operator, adaptive explorer, experiment-ready modeler, agentic digital twin, and self-extending intelligence. We further map emerging agentic systems onto the framework and identify key scientific and technological challenges toward higher autonomy. CMA-AL provides a common language for characterizing agentic computational materials discovery, evaluating the maturity of emerging systems, and guiding their evolution toward increasingly autonomous materials discovery.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.36469",
  "author_total": 3,
  "authors": [
    {
      "name": "Linggang Zhu"
    },
    {
      "name": "Jian Zhou"
    },
    {
      "name": "Zhimei Sun"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/a24ev4fssytvd.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.36469",
  "fulltext_url": null,
  "id": "a24ev4fssytvd",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.36469",
  "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"
    ],
    [
      "openalex",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7214888528",
  "pdf_url": "https://arxiv.org/pdf/2609.36469",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-29",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/a24ev4fssytvd.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:31Z",
      "native_id": "2609.36469",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.36469"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.mtrl-sci",
      "subfield": "condensed-matter"
    }
  ],
  "title": "From Automated Simulation to Autonomous Discovery: A Hierarchical Framework for Agentic Computational Materials Science",
  "title_text": "From Automated Simulation to Autonomous Discovery: A Hierarchical Framework for Agentic Computational Materials Science",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:31Z",
  "url": "https://research.draconitic.com/paper/a24ev4fssytvd",
  "venue": "arXiv",
  "versions": []
}