Research Projects

Field-Controlled Evaporation Mechanisms for Surface Atoms

Extremely strong (~10 V/nm) electric fields rupturing atomic bonds is a relatively well-studied concept in the field of molecular chemistry. When extended to crystalline systems, i.e. material surfaces, this concept is known as field evaporation and its exact mechanisms become more challenging to predict. Field evaporation is the central phenomenon that enables atom probe tomography (APT), and obtaining atomically-accurate APT reconstructions will be impossible without an atomically-accurate understanding of how ions initially form and depart from the surface. By performing first-principles calculations on faceted surfaces under extreme fields, we search for such an understanding. more

Learning Dynamics of STEM Data by Enforcing Physical Consistency with Phase-Field Models

artificial intelligence and digitilization

The project’s goal is to synergize experimental phase transformations dynamics, observed via scanning transmission electron microscopy, with phase-field models that will enable us to learn the continuum description of complex material systems directly from experiment. more

Microstructure Data Mining in Atom Probe Tomography via Machine Learning

artificial intelligence and digitilization

Atom probe tomography (APT) provides three dimensional(3D) chemical mapping of materials at sub nanometer spatial resolution. In this project, we develop machine-learning tools to facilitate the microstructure analysis of APT data sets in a well-controlled way. more

Automatic Classification and Feature Extraction from Multi-Dimensional STEM Data

artificial intelligence and digitilization

In order to prepare raw data from scanning transmission electron microscopy for analysis, pattern detection algorithms are developed that allow to identify automatically higher-order feature such as crystalline grains, lattice defects, etc. from atomically resolved measurements. more

Orbital Contrast in Field Ion Microscopy

method development

We simulate the ionization contrast in field ion microscopy arising from the electronic structure of the imaged surface. For this DFT calculations of the electrified surface are combined with the Tersoff-Hamann approximation to electron tunneling. The approach allows to explain the chemical contrast observed for NiRe alloys. more

Quasi-Newton Transition State Optimization

method development

In order to estimate the kinetics of thermally activated processes, one must determine the energy of the transition state. This transition state is a first-order saddle point on the potential energy surface, i.e., it is a maximum along the reaction coordinate, but a minimum with respect to all other directions in configurational space. We have developed an efficient Quasi-Newton algorithm to optimize the structure of the transition state. more

Limits of Empirical Supercell Extrapolation

The supercell approach allows to model defects with efficient periodic boundary models. By making the supercell sufficiently large, in principle, the limit of an single defect can be recovered. In practice, defect-defect interactions are still relevant for affordable system sizes. We demonstrate that empirical extrapolation has its limitations if the underlying physics is not taken into account or not even known. more

Current Research Projects of the Defect Chemistry and Spectroscopy group

Field-Controlled Evaporation Mechanisms for Surface Atoms

Learning Dynamics of STEM Data by Enforcing Physical Consistency with Phase-Field Models

Microstructure Data Mining in Atom Probe Tomography via Machine Learning

Automatic Classification and Feature Extraction from Multi-Dimensional STEM Data

Orbital Contrast in Field Ion Microscopy

Quasi-Newton Transition State Optimization

Limits of Empirical Supercell Extrapolation