arXiv 2408.020712024-08-04cond-mat.mtrl-sciphysics.app-ph

主动学习自主扫描探针显微镜中融合专家知识的科学探索（SEEK）

Scientific Exploration with Expert Knowledge (SEEK) in Autonomous Scanning Probe Microscopy with Active Learning

Utkarsh Pratiush, Hiroshi Funakubo, Rama Vasudevan, Sergei V. Kalinin, Yongtao Liu

AI总结：

针对基于 DKL 的自主 SPM 工作流局限，本文提出 SEEK 方法，将结构库或光谱特征的先验知识与人类偏好作为预期奖励引入主动学习，从而实现更高效、更有针对性的显微探索。

摘要：

显微技术在材料科学、生物学和纳米技术中发挥了至关重要的作用，可提供高分辨率成像，并深入揭示纳米尺度和原子层级的性质。显微实验的自动化与机器学习方法相结合，是一项具有变革意义的进步，能够提升效率、可复现性以及执行复杂实验的能力。我们此前关于扫描探针显微镜（SPM）自主实验的工作，展示了一种利用深度核学习（DKL）发现结构—性质关系的主动学习框架。该方法已在多种显微技术中展现出广泛应用。在此，为克服基于 DKL 的工作流所存在的局限，我们开发了将先验知识和人类兴趣纳入基于 DKL 的工作流的方法，并在 SPM 中实现了这些工作流。通过整合来自结构库或光谱特征的预期奖励，我们提高了自主显微的探索效率，展示了自主显微中更高效、更具针对性的探索。我们在 SPM 中演示了这些方法的应用，但认为这些方法可以无缝应用于其他显微和成像技术。此外，该概念还可适配于广泛自主实验领域中材料发现的通用贝叶斯优化。

英文摘要：

Microscopy techniques have played vital roles in materials science, biology, and nanotechnology, offering high-resolution imaging and detailed insights into properties at nanoscale and atomic level. The automation of microscopy experiments, in combination with machine learning approaches, is a transformative advancement, offering increased efficiency, reproducibility, and the capability to perform complex experiments. Our previous work on autonomous experimentation with scanning probe microscopy (SPM) demonstrated an active learning framework using deep kernel learning (DKL) for structure-property relationship discovery. This approach has demonstrated broad applications in various microscopy techniques. Here, to address limitations of workflows based on DKL, we developed methods to incorporate prior knowledge and human interest into DKL-based workflows and implemented these workflows in SPM. By integrating expected rewards from structure libraries or spectroscopic features, we enhanced the exploration efficiency of autonomous microscopy, demonstrating more efficient and targeted exploration in autonomous microscopy. We demonstrated the application of these methods in SPM, but we suggest that these methods can be seamlessly applied to other microscopy and imaging techniques. Furthermore, the concept can be adapted for general Bayesian optimization in material discovery across a broad range of autonomous experimental fields.

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