标题： 专家级的自驾驶实验室协议翻译 显示英文标题

标题： Expert-level protocol translation for self-driving labs

摘要： 人工智能（AI）模型的最新发展推动了其在科学研究中的应用，但这些发现的验证和探索需要后续的实证实验。 自动驾驶实验室的概念承诺通过自动化实验过程来提升由AI驱动的发现，从而加速这一过程。 然而，将原本为人类理解设计的实验方案转化为机器可解读的格式面临重大挑战。在特定领域的背景下，这些挑战包括从自然语言转向结构化语言的需求，从隐性知识到显性知识的转变，以及在整个实验步骤中保持因果关系和一致性。 目前，实验方案的翻译任务主要依赖领域专家和信息技术专家的手动且劳动密集型参与，使得整个过程耗时费力。 为了解决这些问题，我们提出了一种框架，通过三阶段的工作流程实现实验方案翻译的自动化，该工作流程逐步构建协议依赖图（PDGs），这些图在语法层面上接近结构化，在语义层面上完成构建，并在执行层面上建立连接。 定量和定性评估表明，其性能与人类专家相当，这凸显了其通过提升自动驾驶实验室内的自动化能力来显著加速和普及科学研究过程的潜力。 显示英文摘要

摘要： 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.