Quantifying nanoscale forces using machine learning in dynamic atomic force microscopy.

ABSTRACT: Dynamic atomic force microscopy (AFM) is a key platform that enables topological and nanomechanical characterization of novel materials. This is achieved by linking the nanoscale forces that exist between the AFM tip and the sample to specific mathematical functions through modeling. However, the main challenge in dynamic AFM is to quantify these nanoscale forces without the use of complex models that are routinely used to explain the physics of tip-sample interaction. Here, we make use of machine learning and data science to characterize tip-sample forces purely from experimental data with sub-microsecond resolution. Our machine learning approach is first trained on standard AFM models and then showcased experimentally on a polymer blend of polystyrene (PS) and low density polyethylene (L

SUBMITTER: Chandrashekar A 

PROVIDER: S-EPMC9063738 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature