Active learning—the field of machine learning (ML) dedicated to optimal experiment design—has played a part in science as far back as the 18th century when Laplace used it to guide his discovery of celestial mechanics. In this work, we focus a closed-loop, active learning-driven autonomous system on another major challenge, the discovery of advanced materials against the exceedingly complex synthesis-processes-structure-property landscape. We demonstrate an autonomous materials discovery methodology for functional inorganic compounds which allow scientists to fail smarter, learn faster, and spend less resources in their studies, while simultaneously improving trust in scientific results and machine learning tools. This robot science enables science-over-the-network, reducing the economic impact of scientists being physically separated from their labs. The real-time closed-loop, autonomous system for materials exploration and optimization (CAMEO) is implemented at the synchrotron beamline to accelerate the interconnected tasks of phase mapping and property optimization, with each cycle taking seconds to minutes. We also demonstrate an embodiment of human-machine interaction, where human-in-the-loop is called to play a contributing role within each cycle. This work has resulted in the discovery of a novel epitaxial nanocomposite phase-change memory material.

主动学习（致力于最佳实验设计的机器学习（ML）领域）早在18世纪就已经在科学中发挥了作用，当时拉普拉斯（Laplace）用它来指导他的天体力学发现。在这项工作中，我们将闭环，主动学习驱动的自主系统的重点放在另一个重大挑战上，即针对极其复杂的合成过程，结构特性属性的高级材料的发现。我们展示了一种用于功能性无机化合物的自主材料发现方法，该方法可让科学家们更聪明地失败，更快地学习，并在他们的研究中花费更少的资源，同时提高对科学成果和机器学习工具的信任。这种机器人科学可以通过网络进行科学，减少与实验室物理分离的科学家的经济影响。在同步加速器光束线上实现了实时的用于材料探索和优化的闭环自主系统（CAMEO），以加速相图绘制和属性优化的互连任务，每个周期只需几秒钟到几分钟。我们还演示了人机交互的一个实施例，其中人在循环中被称为在每个周期内发挥重要作用。这项工作导致了新型外延纳米复合相变存储材料的发现。每个周期需要几秒钟到几分钟。我们还演示了人机交互的一个实施例，其中人在循环中被称为在每个周期内发挥重要作用。这项工作导致了新型外延纳米复合相变存储材料的发现。每个周期需要几秒钟到几分钟。我们还演示了人机交互的一个实施例，其中人在循环中被称为在每个周期内发挥重要作用。这项工作导致了新型外延纳米复合相变存储材料的发现。