Energy Materials Modeling
About
The development and use of simulation methods has established itself as an important pillar for the understanding, improvement and development of new energy materials. In many cases, a description on a length or time scale is not sufficient, since microscopic effects have consequences on the mesoscopic and macroscopic scale, and also in the opposite direction. We therefore aim at a description on all relevant scales and develop problem-adapted methods to link them. To this end, computer simulations are combined with analytical methods and machine learning techniques to develop new models.
On the microscopic scale, one focus is on the properties of rechargeable batteries and fuel cell components as well as materials for hydrogen storage. Emphasis is placed on mechanical properties, which not only interact with electrochemical behavior, but also play an important role in the long-term stability of the materials and components. Ab initio methods are used to predict characteristic properties and to combine them with experimental methods within the institute.
On the mesoscale, we develop new phase-field methods for the modeling of microstructure developments, such as solidification or solid-phase transformations. The goal is to develop quantitative descriptions beyond the possibilities of existing models and to combine these with microscopic and thermodynamic descriptions in order to describe, for example, phase transformations in steels.
On the macroscopic scale, we are particularly devoted to modeling friction phenomena by means of coarsened descriptions such as rate-and-state theories. Such approaches can be applied to describe the decohesion between components for energy conversion processes as well as on geological scales.
Research Topics
Property Modeling for Solid Electrolite and Electrode Materials for All-Solid-State Batteries
In contrast to established lithium-ion batteries, solid-state batteries employ solid electrolytes to reach enhanced energy-density values and to avoid critical failure processes, which can lead to fires.
However, such battery concepts require a stronger attention to mechanical properties, since cell components may deform during charging and discharging processes. We therefore calculate the elastic properties of ceramic solid-state electrolytes and electrode materials in function of their composition by means of ab initio methods, i.e. without using empirical data. At bigger scales, this information will be used to understand possible failure processes related to the formation of conductive paths along grain boundaries.
Degradation of High-Temperature Fuel Cells and Electrolyzers
High-temperature oxide cells play and important role for the production and transformation of green hydrogen. For the long-term stability of such cells, a fundamental understanding of the degradation processes is essential. Our team investigate on the thermodynamic-cinetic level how evaporation transitions and reactions lead to precipitation in the cell components, which can cause loss of performances. Such models are complemented by ab initio investigations to capture at atomic level the underlying processes. The so-obtained data are incorporated in mesoscopic investigations, where we use phase-field methods model the oxidation behavior of interconnection steels. Machine-learning methods are also employed for the optimization of established materials, in order to identify potentially suitable material compositions.
Phase Formation in Metallic Alloys
Our team develops phase-field models for the microstructure development, which plays a central role for example in steels. Building on the thermodynamic descriptions, these models serve to describe the dynamics of interfacial motion. Building on thermodynamic descriptions, these models serve to describe the dynamics of the interfacial motion. Since the resulting microstructures are often very complex, an explicit time tracking of the interfaces is not possible, which is avoided by the phase field method. However, the challenge here is to formulate the models in such a way that they lead to quantitative results that are not falsified by numerical effects. The methods we have developed are applied, for example, to solidification processes and solid phase transformations in metallic alloys.
MEMBERS
Last Modified: 31.01.2024