Bond Breaking Kinetics in Mechanically Controlled Break Junction Experiments: A Bayesian Approach

TL;DR

This paper introduces a Bayesian method for analyzing broad, asymmetric distributions in breakjunction experiments, enabling more reliable estimation of kinetic parameters at the atomic scale with less experimental effort.

Contribution

It presents a novel Bayesian approach for interpreting stochastic data in breakjunction experiments, improving accuracy and efficiency in estimating kinetic parameters.

Findings

Reliable estimation of transition state distance, free energy barrier, and curvature using Bayesian reasoning.

Fewer experimental data needed compared to previous methods.

Reassessment of kinetic parameters in atomic-scale structures.

Abstract

Breakjunction experiments allow investigating electronic and spintronic properties at the atomic and molecular scale. These experiments generate by their very nature broad and asymmetric distributions of the observables of interest, and thus a full statistical interpretation is warranted. We show here that understanding the complete distribution is essential for obtaining reliable estimates. We demonstrate this for Au atomic point contacts, where by adopting Bayesian reasoning we can reliably estimate the distance to the transition state, $x{\ddag}$, the associated free energy barrier, ${\Delta}G{\ddag}$, and the curvature $\nu$ of the free energy surface. Obtaining robust estimates requires less experimental effort than with previous methods, fewer assumptions, and thus leads to a significant reassessment of the kinetic parameters in this paradigmatic atomic-scale structure. Our proposed Bayesian reasoning offers a powerful and general approach when interpreting inherently stochastic data that yield broad, asymmetric distributions for which analytical models of the distribution may be developed.