arXiv subjects

Michael Maiworm

Publications and source records attributed to Michael Maiworm.

4 recordsLinked to original sources

arXiv2023

The electrostatic potential of atomic nanostructures on a metal surface

The discrete and charge-separated nature of matter - electrons and nuclei - results in local electrostatic fields that are ubiquitous in nanoscale structures and are determined by their shape, material, and environment. Such fields are relevant in catalysis, nanoelectronics and quantum nanoscience, and their control will become even more important as the devices in question reach few-nanometres dimensions. Surface-averaging techniques provide only limited experimental access to these potentials at and around individual nanostructures. Here, we use scanning quantum dot microscopy to investigate how electric potentials evolve as nanostructures are built up atom by atom. We image the potential over adatoms, chains, and clusters of Ag and Au atoms on Ag(111) and quantify their surface dipole moments. By focusing on the total charge density, these data establish a new benchmark for ab initio calculations. Indeed, our density functional theory calculations not only show an impressive agreement with experiment, but also allow a deeper analysis of the mechanisms behind the dipole formation, their dependence on fundamental atomic properties and on the atomic configuration of the nanostructures. This allows us to formulate an intuitive picture of the basic mechanisms behind dipole formation, which enables better design choices for future nanoscale systems such as single atom catalysts.

cond-mat.mtrl-sci↗

arXiv2021

Control of Scanning Quantum Dot Microscopy

Scanning quantum dot microscopy is a recently developed high-resolution microscopy technique that is based on atomic force microscopy and is capable of imaging the electrostatic potential of nanostructures like molecules or single atoms. Recently, it could be shown that it not only yields qualitatively but also quantitatively cutting edge images even on an atomic level. In this paper we present how control is a key enabling element to this. The developed control approach consists of a two-degree-of-freedom control framework that comprises a feedforward and a feedback part. For the latter we design two tailored feedback controllers. The feedforward part generates a reference for the current scanned line based on the previously scanned one. We discuss in detail various aspects of the presented control approach and its implications for scanning quantum dot microscopy. We evaluate the influence of the feedforward part and compare the two proposed feedback controllers. The proposed control algorithms speed up scanning quantum dot microscopy by more than a magnitude and enable to scan large sample areas.

eess.SY↗

arXiv2020

Online learning-based Model Predictive Control with Gaussian Process Models and Stability Guarantees

Model predictive control allows to provide high performance and safety guarantees in the form of constraint satisfaction. These properties, however, can be satisfied only if the underlying model, used for prediction, of the controlled process is sufficiently accurate. One way to address this challenge is by data-driven and machine learning approaches, such as Gaussian processes, that allow to refine the model online during operation. We present a combination of an output feedback model predictive control scheme and a Gaussian process-based prediction model that is capable of efficient online learning. To this end, the concept of evolving Gaussian processes is combined with recursive posterior prediction updates. The presented approach guarantees recursive constraint satisfaction and input-to-state stability with respect to the model-plant mismatch. Simulation studies underline that the Gaussian process prediction model can be successfully and efficiently learned online. The resulting computational load is significantly reduced via the combination of the recursive update procedure and by limiting the number of training data points while maintaining good performance.

eess.SY↗

arXiv2020

Fusing Online Gaussian Process-Based Learning and Control for Scanning Quantum Dot Microscopy

Elucidating electrostatic surface potentials contributes to a deeper understanding of the nature of matter and its physicochemical properties, which is the basis for a wide field of applications. Scanning quantum dot microscopy, a recently developed technique allows to measure such potentials with atomic resolution. For an efficient deployment in scientific practice, however, it is essential to speed up the scanning process. To this end we employ a two-degree-of-freedom control paradigm, in which a Gaussian process is used as the feedforward part. We present a tailored online learning scheme of the Gaussian process, adapted to scanning quantum dot microscopy, that includes hyperparameter optimization during operation to enable fast and precise scanning of arbitrary surface structures. For the potential application in practice, the accompanying computational cost is reduced evaluating different sparse approximation approaches. The fully independent training conditional approximation, used on a reduced set of active training data, is found to be the most promising approach.

eess.SY↗