How scanning probe microscopy can be supported by artificial intelligence and quantum computing?

IF 2 3区工程技术 Q2 ANATOMY & MORPHOLOGY

Microscopy Research and Technique Pub Date : 2024-06-12 DOI:10.1002/jemt.24629

Agnieszka Pregowska, Agata Roszkiewicz, Magdalena Osial, Michael Giersig

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Abstract

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.

Abstract Image

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人工智能和量子计算如何支持扫描探针显微镜？

人工智能（AI）的影响正在迅速扩大，给科学和社会都带来了革命性的变化。人工智能几乎应用于生活、科学和技术的所有领域，包括材料科学。扫描探针显微镜（SPM）是广泛应用的技术之一。SPM 通过提供原子精度表面绘图工具，从根本上改变了材料工程、生物和化学领域。尽管扫描探针显微镜有很多优点，但它也有一些缺点，如扫描时间长或可能损坏软表面材料。在本文中，我们将重点关注支持基于 SPM 的测量的潜力，重点是基于人工智能的算法（尤其是基于机器学习的算法）以及量子计算（QC）的应用。研究发现，人工智能有助于实现常规操作中实验过程的自动化、通过算法搜索最佳样本区域以及阐明结构-性质关系。因此，它有助于提高光学纳米镜扫描探针的效率和准确性。此外，基于人工智能的算法与质量控制的结合在提高 SPM 的实际应用方面具有巨大潜力。我们还讨论了基于人工智能-质量控制方法的局限性。最后，我们概述了改进 AI-QC 驱动的 SPM 的研究路径。研究亮点：人工智能和量子计算为扫描探针显微镜提供支持。分析指出了扫描探针显微镜领域的研究空白。该研究旨在阐明人工智能-量子计算驱动的扫描探针显微镜。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Microscopy Research and Technique 医学-解剖学与形态学

CiteScore

5.30

自引率

20.00%

发文量

233

审稿时长

4.7 months

期刊介绍： Microscopy Research and Technique (MRT) publishes articles on all aspects of advanced microscopy original architecture and methodologies with applications in the biological, clinical, chemical, and materials sciences. Original basic and applied research as well as technical papers dealing with the various subsets of microscopy are encouraged. MRT is the right form for those developing new microscopy methods or using the microscope to answer key questions in basic and applied research.