Optimization of experimental materials synthesis and characterization through active learning methods has been growing over the last decade, with examples ranging from measurements of diffraction on combinatorial alloys at synchrotrons, to searches through chemical space with automated synthesis robots for perovskites. In virtually all cases, the target property of interest for optimization is defined apriori with limited human feedback during operation. In contrast, here we present the development of a new type of human in the loop experimental workflow, via a Bayesian optimized active recommender system (BOARS), to shape targets on the fly, employing human feedback. We showcase examples of this framework applied to pre-acquired piezoresponse force spectroscopy of a ferroelectric thin film, and then implement this in real time on an atomic force microscope, where the optimization proceeds to find symmetric piezoresponse amplitude hysteresis loops. It is found that such features appear more affected by subsurface defects than the local domain structure. This work shows the utility of human-augmented machine learning approaches for curiosity-driven exploration of systems across experimental domains. The analysis reported here is summarized in Colab Notebook for the purpose of tutorial and application to other data: https://github.com/arpanbiswas52/varTBO

翻译：通过主动学习方法优化实验材料合成与表征的研究在过去十年中持续增长，应用实例包括在同步辐射装置中对组合合金进行衍射测量，以及利用自动化合成机器人搜索钙钛矿化学空间。在几乎所有案例中，待优化的目标属性均在操作前预先定义，且运行过程中人类反馈极其有限。与此相反，本文提出一种新型人在环实验工作流——贝叶斯优化主动推荐系统（BOARS），通过引入人类反馈动态调整优化目标。我们展示了该框架应用于预采集铁电薄膜压电响应力谱的实例，并进一步将其实时集成到原子力显微镜中，实现对称压电响应幅度滞回曲线的优化。研究发现，此类特征受亚表面缺陷的影响比局部畴结构更显著。本工作揭示了人机增强机器学习方法在跨实验领域开展好奇心驱动系统探索的应用价值。为便于教学推广及应用于其他数据，本文的分析总结于Colab Notebook中：https://github.com/arpanbiswas52/varTBO