揭示初始选择与循环内干预对自主扫描探针显微镜中学习动态的影响
Unraveling the Impact of Initial Choices and In-Loop Interventions on Learning Dynamics in Autonomous Scanning Probe Microscopy
AI总结：
本文分析了自主扫描探针显微镜中初始实验条件与循环内干预对深度核学习动态的影响，提出种子点干预方法，揭示了初始选择对学习轨迹的重要作用，为优化自动化材料表征工作流程提供了指导。
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开展AE。这需要明确定义的方法来指导AE过程，包括超参数调优策略和工作流循环内的高级人工干预。本文全面分析了初始实验条件和循环内干预对扫描探针显微镜AE领域中深度核学习（DKL）学习动态的影响。我们探讨了“种子效应”的概念，即初始实验设置对后续学习轨迹产生重大影响。此外，我们引入了AE中的种子点干预方法，允许操作员影响探索过程。使用PbTiO3薄膜的压电力显微镜（PFM）数据集，我们说明了“种子效应”和循环内种子干预对DKL预测材料属性有效性的影响。该研究强调了初始选择和自适应干预在优化学习率和提升自动化材料表征效率方面的重要性。这项工作为在显微镜学中设计更稳健有效的AE工作流程提供了宝贵见解，并在各种表征技术中具有潜在应用。支持本研究的分析代码公开于https://github.com/Slautin/2024_Seed_effect_DKL_BO。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
