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Atomic Force Microscopy

Atomic Force Microscopy

Atomic force microscopy (AFM) is a type of scanning probe microscopy that can analyze topographic details of various specimens like ceramics, glass, polymers, and biological samples. AFM offers over 1000 times more resolution than the optical imaging system. Images generated from AFM are three-dimensional surface profiles, offering an advantage over the flat, two-dimensional images from other imaging techniques.
The AFM Probe
The probe is regarded as the heart of any AFM setup and comprises the...

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Related Experiment Video

Updated: May 18, 2026

Extracting the Young's Modulus of Native Murine Pulmonary Basement Membranes from Atomic Force Microscopy Derived Force Maps

Extracting the Young's Modulus of Native Murine Pulmonary Basement Membranes from Atomic Force Microscopy Derived Force Maps

Published on: January 31, 2025

Machine learning for predicting young's modulus of soft tissues from atomic force microscopy data.

Maryam Abdoli¹, Moein Taheri²

¹Student of Mechatronics, Department of Electronic Engineering, Faculty of Engineering, Arak University, Arak, Iran.

Journal of Structural Biology

|May 16, 2026

Summary

This study introduces a machine learning model to quickly analyze atomic force microscopy data, making it easier to determine the mechanical properties of biological tissues like Young's modulus.

Keywords:

Atomic force microscopy Contact model Machine learning Young’s modulus

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Micro-Mechanical Characterization of Lung Tissue Using Atomic Force Microscopy

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Published on: August 28, 2011

Area of Science:

Materials Science
Biophysics
Computational Biology

Background:

Atomic force microscopy (AFM) is crucial for assessing the mechanical properties of biological tissues.
Analyzing AFM force-indentation data to determine Young's modulus is complex and requires specialized expertise.
Current data analysis methods for AFM are time-intensive and pose a barrier to widespread adoption.

Purpose of the Study:

To develop a machine learning model for efficient prediction of Young's modulus from AFM force-indentation curves.
To specifically target Young's modulus values in the range of 1 to 50 kPa, relevant for biological materials.
To simplify and accelerate the analysis of AFM nano-indentation data.

Main Methods:

A customized Multi-Layer Perceptron (MLP) regressor was developed.
The model was trained exclusively on synthetic force-indentation datasets generated using Johnson-Kendall-Roberts (JKR) contact theory.
Hyperparameter optimization was performed using a Grid-Search approach.
Model performance was validated against theoretical and experimental AFM force-indentation curves.

Main Results:

The developed MLP regressor accurately predicts Young's modulus from force-indentation curves.
The model demonstrates optimized computational efficiency.
Validation confirmed the regressor's accuracy against both theoretical and laboratory AFM data.

Conclusions:

Machine learning, specifically deep learning approaches, can significantly enhance AFM data analysis.
This customized regressor offers a precise and computationally efficient method for evaluating AFM nano-indentations.
The findings suggest a pathway to overcome current limitations in AFM data interpretation, facilitating broader application in biological material characterization.