We demonstrate the first live, autonomous control over neutron diffraction experiments by developing and deploying ANDiE: the autonomous neutron diffraction explorer. Neutron scattering is a unique and versatile characterization technique for probing the magnetic structure and behavior of materials. However, instruments at neutron scattering facilities in the world is limited, and instruments at such facilities are perennially oversubscribed. We demonstrate a significant reduction in experimental time required for neutron diffraction experiments by implementation of autonomous navigation of measurement parameter space through machine learning. Prior scientific knowledge and Bayesian active learning are used to dynamically steer the sequence of measurements. We show that ANDiE can experimentally determine the magnetic ordering transition of both MnO and Fe1.09Te all while providing a fivefold enhancement in measurement efficiency. Furthermore, in a hypothesis testing post-processing step, ANDiE can determine transition behavior from a set of possible physical models. ANDiE's active learning approach is broadly applicable to a variety of neutron-based experiments and can open the door for neutron scattering as a tool of accelerated materials discovery.

中文翻译：

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通过物理知识贝叶斯主动学习对中子衍射进行实时自主控制

我们通过开发和部署 ANDiE：自主中子衍射探索器，首次展示了对中子衍射实验的实时自主控制。中子散射是一种独特且用途广泛的表征技术，用于探测材料的磁性结构和行为。然而，世界上中子散射设施的仪器是有限的，而且这些设施的仪器常年被超额认购。通过机器学习实现测量参数空间的自主导航，我们证明了中子衍射实验所需的实验时间显着减少。先验科学知识和贝叶斯主动学习用于动态控制测量序列。1.09同时提供五倍的测量效率提升。此外，在假设检验后处理步骤中，ANDiE 可以从一组可能的物理模型中确定过渡行为。ANDiE 的主动学习方法广泛适用于各种基于中子的实验，并且可以打开中子散射作为加速材料发现工具的大门。