用LLM智能体在原子层加工中自主发现化学交互规律
LLM Agents for Knowledge Discovery in Atomic Layer Processing
- 将LLM作为独立智能体，通过试错探索黑箱系统行为
- 在有限探测条件下发现多种化学相互作用模式
- 适合材料科学中的无监督知识挖掘与自动化实验设计
大语言模型（LLMs）近年来备受关注，其作为独立推理智能体的应用也逐渐兴起。本文测试此类智能体在材料科学中进行知识发现的潜力。我们复用LangGraph的工具功能，为智能体提供一个黑箱函数以供探查。与过程优化或执行特定任务不同，知识发现旨在自由探索系统，提出并验证关于黑箱行为的通用性陈述，目标是生成可泛化的科学认知。我们通过一个儿童游戏类比证明了该方法的可行性，展示了试错与坚持在知识发现中的作用，以及结果的高度路径依赖性。随后，我们将相同策略应用于高级原子层加工反应器模拟，仅使用有限探测能力，在无明确指令情况下，成功探索、发现并利用多种化学相互作用。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have garnered significant attention for several years now. Recently, their use as independently reasoning agents has been proposed. In this work, we test the potential of such agents for knowledge discovery in materials science. We repurpose LangGraph's tool functionality to supply agents with a black box function to interrogate. In contrast to process optimization or performing specific, user-defined tasks, knowledge discovery consists of freely exploring the system, posing and verifying statements about the behavior of this black box, with the sole objective of generating and verifying generalizable statements. We provide proof of concept for this approach through a children's parlor game, demonstrating the role of trial-and-error and persistence in knowledge discovery, and the strong path-dependence of results. We then apply the same strategy to show that LLM agents can explore, discover, and exploit diverse chemical interactions in an advanced Atomic Layer Processing reactor simulation using intentionally limited probe capabilities without explicit instructions.
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