Featured projects (1)
Modeling and simulation of materials is a complex problem. Multi-scale, multi-physical, and often nonlinear models are typically hierarchically or concurrently coupled together to capture the effects of nano- and microstructure on the material properties. The conventional methods to numerically solve the governing equations in such systems are computationally heavy. Machine learning methods can accelerate the solution of the equations by orders of magnitude, or efficiently transfer information between scales, domains, and models. This project aims to further investigate and develop such approaches.
Featured research (3)
High-entropy alloys are solid solutions of multiple principal elements that are capable of reaching composition and property regimes inaccessible for dilute materials. Discovering those with valuable properties, however, too often relies on serendipity, because thermodynamic alloy design rules alone often fail in high-dimensional composition spaces. We propose an active learning strategy to accelerate the design of high-entropy Invar alloys in a practically infinite compositional space based on very sparse data. Our approach works as a closed-loop, integrating machine learning with density-functional theory, thermodynamic calculations, and experiments. After processing and characterizing 17 new alloys out of millions of possible compositions, we identified two high-entropy Invar alloys with extremely low thermal expansion coefficients around 2 × 10-6 per degree kelvin at 300 kelvin. We believe this to be a suitable pathway for the fast and automated discovery of high-entropy alloys with optimal thermal, magnetic, and electrical properties.
While age-hardened austenitic high-Mn and high-Al lightweight steels exhibit excellent strength-ductility combinations, their properties are strongly degraded when mechanically loaded under harsh environments, e.g. with the presence of hydrogen (H). The H embrittlement in this type of materials, especially pertaining to the effect of κ-carbide precipitation, has been scarcely studied. Here we focus on this subject, using a Fe-28.4Mn-8.3Al-1.3C (wt.%) steel in different microstructure conditions, namely, solute solution treated and age-hardened. Contrary to the reports that grain boundary (GB) κ-carbides precipitate only during overaging, site-specific atom probe tomography and scanning transmission electron microscopy (STEM) reveal the existence of nanosized GB κ-carbides at early stages of aging. We correlate this observation with the deterioration of H embrittlement resistance in aged samples. While H pre-charged solution-treated samples fail by intergranular fracture at depths consistent with the H ingress depth (∼20 µm), age-hardened samples show intergranular fracture features at a much larger depth of above 500 µm, despite similar amount of H introduced into the material. This difference is explained in terms of the facile H-induced decohesion of GB κ-carbides/matrix interfaces where H can be continuously supplied through internal short-distance diffusion to the propagating crack tips. The H-associated decohesion mechanisms are supported by a comparison with the fracture behavior in samples loaded under the cryogenic temperature and can be explained based on dislocation pileups and elastic misfit at the GB κ-carbide/matrix interfaces. The roles of other plasticity-associated H embrittlement mechanisms are also discussed in this work based on careful investigations of the dislocation activities near the H-induced cracks. Possible alloying and microstructure design strategies for the enhancement of the H embrittlement resistance in this alloy family are also suggested.
- Max-Planck Institut für Eisenforschung
About Dierk Raabe
- I am interested in the design of metallic materials and their microstructures with advanced property profiles. For this I develop and use thermodynamics, microstructure-property models, microstructure physics and atom probe tomography. I also work on sustainable metallurgy. https://damask.mpie.de/ https://damask3.mpie.de/ https://www.mpie.de/microstructure-physics-and-alloy-design