Md Ashiqur Rahman Laskar, Umberto Celano
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摘要

扫描探针显微镜(SPM)彻底改变了我们探索纳米级世界的能力,使材料在原子和分子水平上的成像、操作和表征成为可能。然而,传统的SPM技术存在数据采集速度慢、信噪比低、数据分析复杂等局限性。近年来,机器学习(ML)领域已经成为分析复杂数据集和提取多个领域中有意义的模式和特征的强大工具。机器学习与SPM技术的结合有可能克服传统SPM方法的许多局限性,并为纳米级研究开辟新的机会。在这篇综述文章中,我们将概述基于ml的SPM的最新发展,包括其在地形成像、表面表征和二次成像模式(如电、光谱和机械数据集)中的应用。我们还将讨论将机器学习与SPM技术集成的挑战和机遇,并强调这一跨学科领域对科学和工程各个领域的潜在影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Scanning probe microscopy in the age of machine learning
Scanning probe microscopy (SPM) has revolutionized our ability to explore the nanoscale world, enabling the imaging, manipulation, and characterization of materials at the atomic and molecular level. However, conventional SPM techniques suffer from limitations, such as slow data acquisition, low signal-to-noise ratio, and complex data analysis. In recent years, the field of machine learning (ML) has emerged as a powerful tool for analyzing complex datasets and extracting meaningful patterns and features in multiple fields. The combination of ML with SPM techniques has the potential to overcome many of the limitations of conventional SPM methods and unlock new opportunities for nanoscale research. In this review article, we will provide an overview of the recent developments in ML-based SPM, including its applications in topography imaging, surface characterization, and secondary imaging modes, such as electrical, spectroscopic, and mechanical datasets. We will also discuss the challenges and opportunities of integrating ML with SPM techniques and highlight the potential impact of this interdisciplinary field on various fields of science and engineering.
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