Project Details
Projekt
Atomistic simulations of H trapping at grain boundaries in ferritic alloys
Subject Area
Computer-Aided Design of Materials and Simulation of Materials Behaviour from Atomic to Microscopic Scale
Term
since 2023
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 535248809
An effective hydrogen strategy for a climate-neutral energy production requires the availability of an infrastructure for connecting supply and demand. Along the chain of H production, storage and transport, the many interactions of H atoms with metallic microstructures and the resulting degradation phenomena, summarized by the term hydrogen embrittlement, are posing a considerable challenge. Common origin of most hydrogen embrittlement mechanisms is the high mobility of H atoms in metallic microstructures. Hence, a major step towards an optimization of metallic materials for hydrogen technology is the reliable prediction of H trapping and distribution in the microstructure. In this project, we focus on understanding the solubility of H at grain boundaries (GBs) in iron and ferritic alloys. Our objective is to relate the preference of H for different segregation sites at GBs, i.e. the segregation energy, to the local atomic environment around the H atom in terms of local geometry, chemistry and stress/strain state. To this end, we construct a database of GB structures and energies via a systematic sampling of the space of the GB degrees of freedom in body centered cubic crystal lattices. To derive structure/property relationships from this data, we will develop a set of analysis tools using structure based models in the spirit of a structural unit model, as well as more abstract numerical representations of the local atomic environment. This way, we want to provide on the one hand an intuitive characterization scheme of GBs, which allows a qualitative prediction of GB properties, such as their mobility and trapping potential, and on the other hand the descriptors for a quantitative prediction of GB energies and segregation energies, i.e. the generalized chemical potential of H, also of general grain boundaries, via a machine learning approach.
DFG Programme
Research Grants
