标题： 揭开初始选择和循环内干预对自主扫描探针显微镜学习动态的影响 显示英文标题

标题： Unraveling the Impact of Initial Choices and In-Loop Interventions on Learning Dynamics in Autonomous Scanning Probe Microscopy

摘要： 当前自主实验（AE）的重点是开发稳健的工作流程，以有效地进行AE。 这需要有明确的方法来指导AE过程，包括在工作流循环中的超参数调整策略和高层次的人类干预。 本文对初始实验条件和循环内干预在扫描探针显微镜领域自主实验中深度核学习（DKL）的学习动态的影响进行了全面分析。 我们探讨了“种子效应”的概念，其中初始实验设置对后续学习轨迹有显著影响。 此外，我们引入了一种在AE中的种子点干预方法，使操作员能够影响探索过程。 使用来自PbTiO3薄膜压电响应力显微镜（PFM）的数据集，我们展示了“种子效应”和循环内种子干预对DKL预测材料性能的效果的影响。 该研究强调了初始选择和适应性干预在优化学习速率和提高自动材料表征效率中的重要性。 这项工作为设计更稳健和有效的显微镜AE工作流程提供了有价值的见解，并有望在各种表征技术中应用。 支持资金的分析代码可在https://github.com/Slautin/2024_Seed_effect_DKL_BO公开获得。 显示英文摘要

摘要： The current focus in Autonomous Experimentation (AE) is on developing robust workflows to conduct the AE effectively. This entails the need for well-defined approaches to guide the AE process, including strategies for hyperparameter tuning and high-level human interventions within the workflow loop. This paper presents a comprehensive analysis of the influence of initial experimental conditions and in-loop interventions on the learning dynamics of Deep Kernel Learning (DKL) within the realm of AE in Scanning Probe Microscopy. We explore the concept of 'seed effect', where the initial experiment setup has a substantial impact on the subsequent learning trajectory. Additionally, we introduce an approach of the seed point interventions in AE allowing the operator to influence the exploration process. Using a dataset from Piezoresponse Force Microscopy (PFM) on PbTiO3 thin films, we illustrate the impact of the 'seed effect' and in-loop seed interventions on the effectiveness of DKL in predicting material properties. The study highlights the importance of initial choices and adaptive interventions in optimizing learning rates and enhancing the efficiency of automated material characterization. This work offers valuable insights into designing more robust and effective AE workflows in microscopy with potential applications across various characterization techniques. The analysis code that supports the funding is publicly available at https://github.com/Slautin/2024_Seed_effect_DKL_BO.