The Ehrlich-Schwoebel barrier on an oxide surface: a combined Monte-Carlo and in situ scanning tunneling microscopy approach

Multi-facets of kinetic roughening of interfaces

In this review, the authors are going to explore the intriguing aspects of kinetic roughening of interfaces. Interface roughness dynamics connected with various physical processes have been studied through novel microscopic models in connection with experiments. The statistical properties of such rough interfaces appearing in wide range of physical systems are observed to belong to different universality classes characterized by the scaling exponents. With the advancement of characterization techniques, the scaling exponents of thin-film surface (or the morphological evolution of amorphous surfaces eroded by ion bombardment) are easily computed even in situ during the growing (erosion) conditions. The relevant key physical parameters during the dynamics crucially control the overall scaling behaviour as well as the scaling exponents. The non-universal nature of scaling exponents is emphasized on the variations of the physical parameters in experimental studies and also in theoretical models. Overall, this review containing both theoretical and experimental results will unfold some novel features of surface morphology and its evolution and shed important directions to build an appropriate theoretical framework to explain the observations in systematic and consistent experiments.

Knowledge Extraction from Atomically Resolved Images

Tremendous strides in experimental capabilities of scanning transmission electron microscopy and scanning tunneling microscopy (STM) over the past 30 years made atomically resolved imaging routine. However, consistent integration and use of atomically resolved data with generative models is unavailable, so information on local thermodynamics and other microscopic driving forces encoded in the observed atomic configurations remains hidden. Here, we present a framework based on statistical distance minimization to consistently utilize the information available from atomic configurations obtained from an atomically resolved image and extract meaningful physical interaction parameters. We illustrate the applicability of the framework on an STM image of a FeSe_xTe_1-x superconductor, with the segregation of the chalcogen atoms investigated using a nonideal interacting solid solution model. This universal method makes full use of the microscopic degrees of freedom sampled in an atomically resolved image and can be extended via Bayesian inference toward unbiased model selection with uncertainty quantification.