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News/Lang2MLIP: End-to-End Language-to-Machine Learning Interatomic Potential Development with Autonomous Agentic Workflows
arxiv
PublishedMay 15, 2026 at 4:00 AM
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Lang2MLIP: End-to-End Language-to-Machine Learning Interatomic Potential Development with Autonomous Agentic Workflows

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Publisher summary· verbatim

arXiv:2605.14527v1 Announce Type: new Abstract: Developing machine learning interatomic potentials (MLIPs) for complex materials systems remains challenging because it requires expertise in atomistic simulations, machine learning, and workflow design, as well as iterative active learning procedures.

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Discussion
Mentioned models
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    Lang2MLIP
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    large language models (LLMs)
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#machine-learning#materials-science#automated-pipelines

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Mentioned models
02
  • 01
    Lang2MLIP
  • 02
    large language models (LLMs)
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
03
#machine-learning#materials-science#automated-pipelines

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