AI 中文总结
该研究提出前瞻性轨迹架构，通过同步样本、仪器与决策历史，结合从AFM/PFM档案重建的仪器轨迹，为主动自主科学实验提供支撑，助力可复现自主性等多方面应用。
AI 中文摘要
人工智能正将科学仪器转变为主动系统，在这类系统中，观测结果可决定后续测量内容。我们认为这会产生一种额外的科学记录，即实验轨迹，它是对样本溯源、采集数据、元数据及分析工作流程的补充。仪器轨迹最终应与描述样本演化的样本轨迹、记录人类或算法选择的决策轨迹同步。我们从2023-2026年包含11.8万条带时间戳事件的纵向AFM/PFM档案中回溯重建了一条仪器轨迹。常规保存文件可揭示材料实验活动、潜在探针与校准状态、会话级复杂性、实验决策语法及复合调谐操作，还会暴露出缺失的内容，包括未保存的调谐操作与故障、明确的样本/探针标识、完整时间信息、外生状态及决策依据。因此，我们提出一种前瞻性轨迹架构，用于记录同步的样本、仪器及决策历史，支持可复现的自主性、预测性维护、反事实分析、操作员培训及跨设施迁移。
英文摘要
Artificial intelligence is turning scientific instruments into active systems in which observations can determine what is measured next. We argue that this creates an additional scientific record, the experimental trajectory, complementing sample provenance, acquired data and metadata, and analysis workflows. Instrument Traces should ultimately be synchronized with Sample Traces describing specimen evolution and Decision Traces recording human or algorithmic choices. We reconstruct an Instrument Trace retrospectively from a longitudinal AFM/PFM archive containing 118,000 timestamped events from 2023-2026. Conventional saved files reveal material campaigns, latent probe and calibration states, session-level complexity, experimental decision grammar, and composite tuning actions. They also expose what is missing, including unsaved tuning and failures, explicit sample/probe identities, complete timing, exogenous state, and decision rationale. We therefore propose a prospective trace architecture that records synchronized sample, instrument, and decision histories, enabling reproducible autonomy, predictive maintenance, counterfactual analysis, operator training, and transfer across facilities.
