Machine Learning Approaches to Quantify Nanoscale Variations in the Mechanical Properties of Soft Nanoparticles

Abstract

We use machine learning to analyze atomic force microscopy-force spectroscopy (AFM-FS) measurements of the mechanical properties of soft nanoparticles on a hard substrate. We compare two approaches based on the manual selection of features that describe various aspects of the mechanical properties measured using AFM-FS – one that uses an extreme gradient boosting algorithm (supervised learning), and the other based on k-means clustering (unsupervised learning) to classify the force-distance curves according to the features. We used these approaches to generate two machine learning (ML) classifiers – one to differentiate between the soft nanoparticles and the hard substrate, and the other to identify structure within individual nanoparticles based on nanoscale variations in their mechanical properties. After training the classifiers on data from just two AFM images, we found that both approaches were successful in correctly identifying individual soft nanoparticles within the AFM-FS scans, whereas the supervised approach was more successful in correctly identifying and quantifying stiffer inner regions within the nanoparticles. The results of our study show that ML strategies can be used to accurately and efficiently characterize nanoscale variations in the mechanical properties of soft, biological materials.