Quantifying nanoscale forces using machine learning in dynamic atomic force microscopy-Reference-Cited by-同舟云学术

Quantifying nanoscale forces using machine learning in dynamic atomic force microscopy

Published:2022 Issue:9 Volume:4 Page:2134-2143
ISSN:2516-0230
Container-title:Nanoscale Advances
language:en
Short-container-title:Nanoscale Adv.

Author:

Chandrashekar Abhilash¹^ORCID,Belardinelli Pierpaolo²^ORCID,Bessa Miguel A.³,Staufer Urs¹,Alijani Farbod¹

Affiliation:

1. Precision and Microsystems Engineering, TU Delft, Delft, The Netherlands

2. DICEA, Polytechnic University of Marche, Ancona, Italy

3. Materials Science and Engineering, TU Delft, Delft, The Netherlands

Abstract

In this article we make use of data-science and machine learning to distil physically interpretable models from AFM measurements and predict nanoscale interaction forces with sub-microsecond resolution directly from experimental data.

Funder

Nederlandse Organisatie voor Wetenschappelijk Onderzoek

Horizon 2020

Publisher

Royal Society of Chemistry (RSC)

Subject

General Engineering,General Materials Science,General Chemistry,Atomic and Molecular Physics, and Optics,Bioengineering

Link

http://pubs.rsc.org/en/content/articlepdf/2022/NA/D2NA00011C

Reference59 articles.

1. Identification of Nanoscale Dissipation Processes by Dynamic Atomic Force Microscopy

2. Fast nanomechanical spectroscopy of soft matter

3. Discrimination of adhesion and viscoelasticity from nanoscale maps of polymer surfaces using bimodal atomic force microscopy

4. Bimodal frequency-modulated atomic force microscopy with small cantilevers

5. Chemical identification of individual surface atoms by atomic force microscopy

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