Bayesian changepoint analysis for atomic force microscopy and soft material indentation

TL;DR

This paper introduces a hierarchical Bayesian framework for changepoint analysis in material indentation data, enabling accurate inference of mechanical properties like Young's modulus across scales, with automated software implementation.

Contribution

It presents the first rigorous Bayesian inference method for material indentation data, applicable to atomic force microscopy and other experiments, with high-throughput automated algorithms.

Findings

Accurate estimation of Young's modulus across different materials.

Robust changepoint detection in macro- and micro-scale experiments.

Software package BayesCP available for broad use.

Abstract

Material indentation studies, in which a probe is brought into controlled physical contact with an experimental sample, have long been a primary means by which scientists characterize the mechanical properties of materials. More recently, the advent of atomic force microscopy, which operates on the same fundamental principle, has in turn revolutionized the nanoscale analysis of soft biomaterials such as cells and tissues. This paper addresses the inferential problems associated with material indentation and atomic force microscopy, through a framework for the changepoint analysis of pre- and post-contact data that is applicable to experiments across a variety of physical scales. A hierarchical Bayesian model is proposed to account for experimentally observed changepoint smoothness constraints and measurement error variability, with efficient Monte Carlo methods developed and employed to realize inference via posterior sampling for parameters such as Young's modulus, a key quantifier of material stiffness. These results are the first to provide the materials science community with rigorous inference procedures and uncertainty quantification, via optimized and fully automated high-throughput algorithms, implemented as the publicly available software package BayesCP. To demonstrate the consistent accuracy and wide applicability of this approach, results are shown for a variety of data sets from both macro- and micro-materials experiments--including silicone, neurons, and red blood cells--conducted by the authors and others.