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  <title>Zongmin Zhang — Publications &amp; News</title>
  <subtitle>Academic updates from Zongmin Zhang, HKUST CS undergraduate and AI for Science researcher.</subtitle>
  <link href="https://nagatobigseven.github.io/" rel="alternate" type="text/html"/>
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  <id>https://nagatobigseven.github.io/</id>
  <updated>2026-06-18T00:00:00Z</updated>
  <author>
    <name>Zongmin Zhang</name>
    <uri>https://nagatobigseven.github.io/</uri>
  </author>

  <entry>
    <title>AdsMind: A Physics-Grounded Multi-Agent System for Self-Correcting Discovery of Adsorption Configurations on Heterogeneous Catalyst Surfaces</title>
    <link href="https://arxiv.org/abs/2606.19152" rel="alternate" type="text/html"/>
    <id>https://arxiv.org/abs/2606.19152</id>
    <published>2026-06-18T00:00:00Z</published>
    <updated>2026-06-18T00:00:00Z</updated>
    <summary>Led the implementation, benchmark design and cross-backend evaluation of AdsMind, a closed-loop, physics-grounded multi-agent system for autonomous adsorption-configuration discovery on heterogeneous catalyst surfaces. arXiv preprint arXiv:2606.19152, 2026.</summary>
    <category term="publication"/>
  </entry>

  <entry>
    <title>Autonomous Heterogeneous Catalyst Discovery with a Self-Evolving Multi-Agent Digital Twin</title>
    <link href="https://arxiv.org/abs/2606.05050" rel="alternate" type="text/html"/>
    <id>https://arxiv.org/abs/2606.05050</id>
    <published>2026-06-04T00:00:00Z</published>
    <updated>2026-06-04T00:00:00Z</updated>
    <summary>Conducted Codex-based ablation experiments and contributed to the evaluation of CatDT, a self-evolving multi-agent digital-twin framework for autonomous heterogeneous catalyst discovery. arXiv preprint arXiv:2606.05050, 2026.</summary>
    <category term="publication"/>
  </entry>

  <entry>
    <title>From Knowledge to Action: Outcomes of the 2025 LLM Hackathon for Applications in Materials Science and Chemistry</title>
    <link href="https://arxiv.org/abs/2605.03205" rel="alternate" type="text/html"/>
    <id>https://arxiv.org/abs/2605.03205</id>
    <published>2026-05-05T00:00:00Z</published>
    <updated>2026-05-05T00:00:00Z</updated>
    <summary>Large-author community report on LLM-enabled scientific workflows from an international hackathon at the intersection of large language models, materials science, and chemistry. arXiv preprint arXiv:2605.03205, 2026.</summary>
    <category term="publication"/>
  </entry>
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