Pregowska Agnieszka, Roszkiewicz Agata, Osial Magdalena, Giersig Michael
Department of Information and Computational Science, Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland.
Microsc Res Tech. 2024 Nov;87(11):2515-2539. doi: 10.1002/jemt.24629. Epub 2024 Jun 12.
The impact of Artificial Intelligence (AI) is rapidly expanding, revolutionizing both science and society. It is applied to practically all areas of life, science, and technology, including materials science, which continuously requires novel tools for effective materials characterization. One of the widely used techniques is scanning probe microscopy (SPM). SPM has fundamentally changed materials engineering, biology, and chemistry by providing tools for atomic-precision surface mapping. Despite its many advantages, it also has some drawbacks, such as long scanning times or the possibility of damaging soft-surface materials. In this paper, we focus on the potential for supporting SPM-based measurements, with an emphasis on the application of AI-based algorithms, especially Machine Learning-based algorithms, as well as quantum computing (QC). It has been found that AI can be helpful in automating experimental processes in routine operations, algorithmically searching for optimal sample regions, and elucidating structure-property relationships. Thus, it contributes to increasing the efficiency and accuracy of optical nanoscopy scanning probes. Moreover, the combination of AI-based algorithms and QC may have enormous potential to enhance the practical application of SPM. The limitations of the AI-QC-based approach were also discussed. Finally, we outline a research path for improving AI-QC-powered SPM. RESEARCH HIGHLIGHTS: Artificial intelligence and quantum computing as support for scanning probe microscopy. The analysis indicates a research gap in the field of scanning probe microscopy. The research aims to shed light into ai-qc-powered scanning probe microscopy.
人工智能（AI）的影响正在迅速扩大，给科学和社会带来变革。它被应用于生活、科学和技术的几乎所有领域，包括材料科学，而材料科学不断需要新颖的工具来进行有效的材料表征。广泛使用的技术之一是扫描探针显微镜（SPM）。SPM通过提供原子级精确表面测绘工具，从根本上改变了材料工程、生物学和化学。尽管它有许多优点，但也存在一些缺点，比如扫描时间长或可能损坏软表面材料。在本文中，我们关注支持基于SPM测量的潜力，重点是基于AI的算法，特别是基于机器学习的算法以及量子计算（QC）的应用。已经发现，AI有助于在常规操作中自动化实验过程、通过算法搜索最佳样品区域以及阐明结构-性能关系。因此，它有助于提高光学纳米显微镜扫描探针的效率和准确性。此外，基于AI的算法与QC的结合可能具有巨大潜力，可增强SPM的实际应用。还讨论了基于AI-QC方法的局限性。最后，我们概述了改进由AI-QC驱动的SPM的研究路径。研究亮点：人工智能和量子计算对扫描探针显微镜的支持。分析表明扫描探针显微镜领域存在研究空白。该研究旨在阐明由AI-QC驱动的扫描探针显微镜。
