Autonomous experimentation (AE) combines machine learning and research hardware automation in a closed loop, guiding subsequent experiments toward user goals. As applied to materials research, AE can accelerate materials exploration, reducing time and cost compared to traditional Edisonian studies. Additionally, integrating knowledge from diverse sources including theory, simulations, literature, and domain experts can boost AE performance. Domain experts may provide unique knowledge addressing tasks that are difficult to automate. Here, we present a set of methods for integrating human input into an autonomous materials exploration campaign for composition-structure phase mapping. The methods are demonstrated on x-ray diffraction data collected from a thin film ternary combinatorial library. At any point during the campaign, the user can choose to provide input by indicating regions-of-interest, likely phase regions, and likely phase boundaries based on their prior knowledge (e.g., knowledge of the phase map of a similar material system), along with quantifying their certainty. The human input is integrated by defining a set of probabilistic priors over the phase map. Algorithm output is a probabilistic distribution over potential phase maps, given the data, model, and human input. We demonstrate a significant improvement in phase mapping performance given appropriate human input.

翻译：自主实验（AE）将机器学习与研究硬件自动化闭环结合，可引导后续实验朝向用户目标推进。在材料研究领域应用时，AE能加速材料探索，相比传统爱迪生式研究范式显著降低时间与成本。此外，整合来自理论、模拟、文献及领域专家等多源知识可提升AE性能。领域专家能够针对自动化难以完成的任务提供独特知识。本文提出一套将人类输入整合到成分-结构物相图谱自主材料探索中的方法体系。这些方法基于薄膜三元组合库收集的X射线衍射数据进行了验证。在探索过程中，用户可随时基于先验知识（例如相似材料体系物相图谱知识）标注感兴趣区域、可能的物相区域及物相边界，同时量化其置信度。通过定义物相图谱的概率先验分布实现人类输入的融合。算法输出为基于数据、模型与人类输入下潜在物相图谱的概率分布。我们证明，在合理的人类输入条件下，物相图谱映射性能可得到显著提升。