将人工实验流程自动转化为机器可执行的结构化指令。
Expert-level protocol translation for self-driving labs
- 分三阶段构建协议依赖图，逐步实现语法、语义与执行层面的结构化。
- 在定性与定量评估中表现媲美人类专家，显著提升自动化效率。
- 适合需高精度实验流程自动化的智能科研实验室使用。
人工智能模型的发展推动了其在科学发现中的应用，但这些发现的验证与探索仍需后续的实证实验。自驱动实验室的概念旨在自动化这一实验流程。然而，将原本为人类理解设计的实验协议转换为机器可读格式面临巨大挑战，尤其在特定专业领域内，要求协议具备结构化而非自然语言表达、明确而非隐含知识，并保持步骤间的因果一致。目前，协议翻译主要依赖领域专家与信息技术人员的大量手动工作，耗时费力。为此，我们提出一个框架，通过三阶段工作流自动完成协议翻译：逐步构建协议依赖图（PDG），分别在语法层、语义层和执行层实现结构化。定量与定性评估表明，该方法性能可媲美人类专家，展现出显著加速并普及科学发现的潜力，大幅提升自驱动实验室的自动化水平。
原文摘要 · Abstract (English)
Recent development in Artificial Intelligence (AI) models has propelled their application in scientific discovery, but the validation and exploration of these discoveries require subsequent empirical experimentation. The concept of self-driving laboratories promises to automate and thus boost the experimental process following AI-driven discoveries. However, the transition of experimental protocols, originally crafted for human comprehension, into formats interpretable by machines presents significant challenges, which, within the context of specific expert domain, encompass the necessity for structured as opposed to natural language, the imperative for explicit rather than tacit knowledge, and the preservation of causality and consistency throughout protocol steps. Presently, the task of protocol translation predominantly requires the manual and labor-intensive involvement of domain experts and information technology specialists, rendering the process time-intensive. To address these issues, we propose a framework that automates the protocol translation process through a three-stage workflow, which incrementally constructs Protocol Dependence Graphs (PDGs) that approach structured on the syntax level, completed on the semantics level, and linked on the execution level. Quantitative and qualitative evaluations have demonstrated its performance at par with that of human experts, underscoring its potential to significantly expedite and democratize the process of scientific discovery by elevating the automation capabilities within self-driving laboratories.
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