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.

本篇研究论文在自主实验领域提出了关于初始实验条件和干预对深度核学习在扫描探针显微镜中学习动态的影响的综合分析，并通过对PbTiO3薄膜的数据集进行实证研究，突出了初始选择和自适应干预在优化学习速率和提高自动化材料表征效率方面的重要性。

自主扫描探针显微镜中初始选择与循环干预对学习动力学的影响解析