AIMS：一种用于量子物质的不确定性感知人工智能实验者
AIMS: an AI experimentalist turns uncertainty into quantum matter discovery
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中文总结 AI 辅助
研究量子材料实验中的不确定性问题，提出AIMS这一不确定性感知人工智能实验者，通过连接三个嵌套循环实现感知恢复、测量选择和能量尺度分辨机制归因，展示了对量子物质的不确定性感知实验能动性。
中文摘要 AI 辅助
自主科学智能体开始加速发现进程，但多数演示运行于数字或高度结构化环境，其中对象、行动和目标大多预先定义。量子材料实验面临更难问题，存在仪器状态漂移、有用信号仅占非均匀样本稀有区域以及物理机制常不确定等不确定性。本文介绍科学推理与测量人工智能智能体（AIMS），一种用于低温微波阻抗显微镜的不确定性感知闭环人工智能实验者，它将不确定性转化为实验行动。AIMS连接三个嵌套循环：不确定感知下的导航、样本不均匀性下的测量选择以及模糊物理下的尺度分辨机制归因。在导航中，它在低温位移后重新定位样本，标记不可靠位置估计并调用恢复策略，显著减少样本定位时间。在测量中，它绘制扭曲双层MoSe₂的扭曲角分布和广义维格纳晶体分数以识别具有最强相关响应的区域。在发现中，AIMS探究的不是熔化是经典还是量子，而是库仑排斥、跳跃和其他能量尺度的竞争如何塑造观察到的层次结构。通过测试经典极限、改变跳跃和库仑尺度并将样本形态作为次要可测试变量保留，AIMS优先考虑异常稳健的ν = 1/2晶体的量子涨落重整化起源。AIMS在一个闭环中通过感知恢复、测量选择和能量尺度分辨机制归因展示了对量子物质的不确定性感知实验能动性。
英文摘要
Most AI agents act only after scientists have defined the task. Discovery is harder under practical uncertainties: the probe may not be where it is expected, the signal may occupy only a small region of a disordered sample, and the evidence may not distinguish among competing explanations. Here we show that an AI agent can decide what evidence an uncertain experiment needs next, and act on it. Beyond automation, AIMS, an uncertainty-aware experimentalist for cryogenic microwave impedance microscopy, quantifies uncertainty where it originates, in perception, sampling, and interpretation, and converts each into its own corrective action rather than a single confidence score. Given only an open objective, AIMS relocated a probe lost during cooldown while flagging its own unreliable estimates, mapped twist angle disorder to locate the strongest correlated states in twisted bilayer MoSe$_2$, and uncovered a paradox: the half-filled stripe that classical theory predicts should melt first survived longest. Distinguishing an incomplete model from a wrong mechanism, AIMS commissioned a beyond-mean-field calculation and an independent structural measurement as the decisive tests, revising its interpretation as each arrived: quantum motion reverses the classical hierarchy, stabilizing the half-filled stripe while destabilizing its neighbors. These uncertainty-to-action loops are generic to scanning probe experiments, and AIMS turns uncertainty from an obstacle into a driver of discovery.
