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Here are some of my research interests. You find more on our department website at the Max-Planck Institut für Eisenforschung.
  
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Metals & Metallurgical Research

Metallic materials carry human civilization more than 5000 years, lending even entire ages their name. With a global market of 3500 billion € per year and a daily turnover of 3.5 Billion € in the EU alone materials are key drivers in economy (World Trade Organisation, Optimat Materials Landscaping Study).

The accelerated demand for both, load-bearing and functional materials in key sectors such as energy, sustainability, construction, health, communication, infrastructure, safety and transportation is resulting in predicted production growth rates of up to 200 per cent until 2050 for many material classes.

‘Materials’ are a specific type of matter that is finally used for something, be it a product or process. Therefore materials science has generally both a basic and an applied facet. Nowadays, after virtually thousands of years of development, we still use only about 1000 different types of metallic alloys out of a sheer infinite combinatorial space of about 1060 possible combinations when considering only the 50-60 frequently used elements. This means that we only stand at the beginning of basic research of metals.

These boundary conditions require not only to better understand the fundamental relationships between synthesis, manufacturing, basic mechanisms, microstructure and properties but also to discover novel materials that meet both, advanced application challenges under harsh environments.

Currently several developments are revolutionizing materials research. The first one is the availability of models and artificial intelligence methods with predictive capability such as provided by density functional theory, advanced quasi-particle and continuum simulation methods, computational alloy thermodynamics as well as big data driven tools fueled by machine learning approaches. The second one is the availability and correlative use of high resolution characterization tools such as corrected electron microscopes, atom probe tomography, synchrotron and neutron imaging. The third one is materials synthesis, which stretches nowadays from efficient chemical processing of nanoparticles, thin film synthesis, artchitectured materials, combinatorial casting to additive manufacturing providing fast and flexible routes for materials fabrication. 

These enabling techniques meet latest developments in industry and society such as the need for improved sustainability of materials and their manufacturing value chains (e.g. synthesis of alloys and polymers with greenhouse gas emissions); revolutionized drive trains in the transportation sector, i.e the transition from fossile to electrical propelling systems and the way how we provide and store energy.

All these techniques and recent developments enable us to solve some of the most essential challenges in the fields of mobility, energy, infrastructure, medicine and safety

Along these topics my group works on key fields of highest relevance for society and manufacturing. Examples relate to the following fields:

- Energy (e.g., materials for a hydrogen-propelled industry, hydrogen-tolerant structural alloys, catalysis materials, high temperature alloys, semiconducting materials for photovoltaics and photo-electrochemistry, fuel cell components, materials for direct solar-thermic components)
- Mobility (e.g., ductile magnesium, steels and magnets for light weight electrical and hybrid vehicles)
- Infrastructure (e.g., high strength and corrosion-resistant alloys for infrastructures, such as wind turbines and chemical infrastructures)
- Medicine & health (e.g., biomedical tribology, compliant implant alloys)
- Safety (e.g., high toughness alloys, cryogenic alloys, coatings and thin film materials, hydrogen tolerant materials).

 

About 70% of all innovations in Europe are associated with progress in the fields of materials science and engineering. The global market for materials is 3500 billion € per year. About 70% of all innovations in Europe are associated with progress in the fields of materials science and engineering. The global market for materials is 3500 billion € per year.

Some recent papers

Multifunctional high-entropy materials
Entropy-related phase stabilization can allow compositionally complex solid solutions of multiple principal elements. The massive mixing approach was originally introduced for metals and has recently been extended to ionic, semiconductor, polymer and low-dimensional materials. Multielement mixing can leverage new types of random, weakly ordered clustering and precipitation states in bulk materials as well as at interfaces and dislocations. The many possible atomic configurations offer opportunities to discover and exploit new functionalities, as well as to create new local symmetry features, ordering phenomena and interstitial configurations. This opens up a huge chemical and structural space in which uncharted phase states, defect chemistries, mechanisms and properties.
Multifunctional high-entropy.pdf
PDF-Dokument [2.2 MB]
In one single step from oxides to sustainable bulk metalllic alloys
Metal production involves today 3 steps: extracting metals from ores, mixing into alloys by liquid processing and thermomechanical processing to achieve the desired microstructures. This sequential approach, practised since the Bronze Age, reaches its limit today because of the urgent demand for a sustainable economy: almost 10% of all the greenhouse gas emissions (mostly carbon dioxide) are generated because of the use of fossil-based carbon-containing reductants and high-temperature metallurgical processing with frequent intermediate cooling and re-heating. Here we present a novel sustainable hydrogen-based joint redox synthesis and compaction approach that reforms traditional alloymaking by merging metal extraction, reduction from ores, metal alloying and thermomechanical treatment.
Nature Sept 2024 s41586-024-07932-w One[...]
PDF-Dokument [16.1 MB]
Green steel from red mud through climate-neutral hydrogen plasma reduction
Red mud, a byproduct of bauxite refinement into alumina—the feedstock for aluminium production, is a significant environmental concern due to its sheer volume. Globally, approximately 180 million tons of red mud are generated annually, resulting in an alarming stockpile of 4 billion tons. In this study, we propose a novel approach to transform red mud into valuable and sustainable feedstock using fossil-free hydrogen plasma-based reduction. By doing so, we address a portion of the steel industry's CO2 emissions by enabling the production of several hundred million tons of green steel. The reduction involves rapid liquid-state reduction, chemical partitioning, and density- and viscosity-driven separation between metal and oxides.
Green steel from red mud.pdf
PDF-Dokument [15.5 MB]
The Materials Science behind Sustainable Metals and Alloys
OPEN ACCESS: https://pubs.acs.org/doi/10.1021/acs.chemrev.2c00799
- - - - - -
Production of metals stands for 40% of all industrial greenhouse gas emissions, 10% of the global energy consumption, 3.2 billion tonnes of minerals mined, and several billion tonnes of by-products every year. Therefore, metals must become more sustainable. A circular economy model does not work, because market demand exceeds the available scrap currently by about twothirds. Even under optimal conditions, at least one-third of the metals will also in the future come from primary production, creating huge emissions. Although the influence of metals on global warming has been discussed with respect to mitigation strategies and socio-economic factors, the materials science has been less addressed.
acs.chemrev.2c00799_with_volume_ID_s.pdf
PDF-Dokument [6.2 MB]

<< Neues Textfeld >>

Reducing Iron Oxide with Ammonia: A Sustainable Path to Green Steel
Iron making is the biggest single cause of global warming. The reduction of iron ores with carbon generates about 7% of the global carbon dioxide emissions to produce ≈1.85 billion tons of steel per year. This dramatic scenario fuels efforts to re-invent this sector by using renewable and carbon-free reductants and electricity. Here, the authors show how to make sustainable steel by reducing solid iron oxides with hydrogen released from ammonia. Ammonia is an annually 180 million ton traded chemical energy carrier, with established transcontinental logistics and low liquefaction costs. It can be synthesized with green hydrogen and release hydrogen again through the reduction reaction. This advantage connects it with green iron making, for replacing fossil reductants.
Advanced Science - 2023 - Ma - Reducing [...]
PDF-Dokument [2.1 MB]
Accelerating the design of compositionally complex materials via physics-informed artificial intelligence
The chemical space for designing materials is practically infinite. This makes disruptive progress by traditional physics-based modeling alone challenging. Yet, training data for identifying composition–structure–property relations by artificial intelligence are sparse. We discuss opportunities to discover new chemically complex materials by hybrid methods where physics laws are combined with artificial intelligence.
a85cb3e6-26ba-420a-88a8-0a3ef2baa7e0.pdf
PDF-Dokument [2.3 MB]
A mechanically strong and ductile soft magnet with extremely low coercivity
Han, L., Maccari, F., Souza Filho, I.R. et al. A mechanically strong and ductile soft magnet with extremely low coercivity. Nature 608, 310–316 (2022). https://doi.org/10.1038/s41586-022-04935-3
2022 Nature A mechanically strong and du[...]
PDF-Dokument [14.0 MB]

Toward the design of ultrahigh-entropy alloys via mining six million texts

 

It has long been a norm that researchers extract knowledge from literature to design materials. However, the avalanche of publications makes the norm challenging to follow. Text mining (TM) is efficient in extracting information from corpora. Still, it cannot discover materials not present in the corpora, hindering its broader applications in exploring novel materials, such as high-entropy alloys (HEAs). Here we introduce a concept of “context similarity" for selecting chemical elements for HEAs, based on TM models that analyze the abstracts of 6.4 million papers. The method captures the similarity of chemical elements in the context used by scientists. It overcomes the limitations of TM and identifies the Cantor and Senkov HEAs. We demonstrate its screening capability for six- and seven-component lightweight HEAs by finding nearly 500 promising alloys out of 2.6 million candidates. The method thus brings an approach to the development of ultrahigh-entropy alloys and multicomponent materials.

Ultrahigh Entropy Alloys discovered from Text Mining
It has long been a norm that researchers extract knowledge from literature to design materials. However, the avalanche of publications makes the norm challenging to follow. Text mining (TM) is efficient in extracting information from corpora. Still, it cannot discover materials not present in the corpora, hindering its broader applications in exploring novel materials, such as high-entropy alloys (HEAs). Here we introduce a concept of “context similarity" for selecting chemical elements for HEAs, based on TM models that analyze the abstracts of 6.4 million papers. The method captures the similarity of chemical elements in the context used by scientists. It overcomes the limitations of TM and identifies the Cantor and Senkov HEAs. We demonstrate its screening capability for six- and seven
s41467-022-35766-5.pdf
PDF-Dokument [2.2 MB]
Corrosion-resistant alloys designed through natural language processing and deep learning
We propose strategies that couple natural language processing with deep learning to enhance machine capability for corrosion-resistant alloy design. First, accuracy of machine learning models for materials datasets is often limited by their inability to incorporate textual data. Manual extraction of numerical parameters from descriptions of alloy processing or experimental methodology inevitably leads to a reduction in information density. To overcome this, we have developed a fully automated natural language processing approach to transform textual data into a form compatible for feeding into a deep neural network. This approach has resulted in a pitting potential prediction accuracy substantially beyond state of the art. Second, we have implemented a deep learning model with a transforme
sciadv.adg7992 - corrosion resistant all[...]
PDF-Dokument [1.5 MB]
Machine learning–enabled high-entropy alloy discovery
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 wit
Machine learning–enabled high-entropy al[...]
PDF-Dokument [1.4 MB]
Strategies for improving the sustainability of structural metals
Nature
Raabe et al Nature Nov 2019 Sustainabili[...]
PDF-Dokument [2.2 MB]
Chemical heterogeneity enhances hydrogen resistance in high-strength steels
Nature Materials
Sun_et_al-2021-Nature_Materials.pdf
PDF-Dokument [2.7 MB]
High-strength Damascus steel by additive manufacturing
Nature | Vol 582 | 25 June 2020
Nanosteel-composite high-strength Damasc[...]
PDF-Dokument [5.2 MB]
The hidden structure dependence of the chemical life of dislocations
Science Advances
Science Advances 2021 The hidden structu[...]
PDF-Dokument [840.7 KB]
Advanced High-Strength Steels
Raabe_et_al-2020-Metallurgical_and_Mater[...]
PDF-Dokument [28.0 MB]
High- entropy alloys
Nature Review Materials
Nature Materials Reviews Aug 2019 High-e[...]
PDF-Dokument [2.4 MB]
Steels in additive manufacturing
MSE 2019 Steels in additive manufacturin[...]
PDF-Dokument [7.7 MB]
Engineering atomic-level complexity in high-entropy and complex concentrated alloys
https://www.nature.com/articles/s41467-019-10012-7
NATURE COMMUNICATIONS | (2019) 10:2090 | https://doi.org/10.1038/s41467-019-10012-7 | www.nature.com/naturecommunications
Oh_et_al-2019-Nature_Communications.pdf
PDF-Dokument [1.5 MB]
DAMASK – The Düsseldorf Advanced Material Simulation Kit for modeling multi-physics crystal plasticity, thermal, and damage phenomena from the single crystal up to the component scale
Computational Materials Science
Volume 158, 15 February 2019, Pages 420-478
DAMASK – Duesseldorf Advanced Material S[...]
PDF-Dokument [10.5 MB]
Enhanced strength and ductility in a high-entropy alloy via ordered oxygen complexes
Nature (November 2018)
Nature 2018 strength ductility high-entr[...]
PDF-Dokument [8.2 MB]
Bidirectional Transformation Enables Hierarchical Nanolaminate Dual-Phase High-Entropy Alloys
Advanced Materials 2018, 1804727
Bidirectional TRIP effect in High Entrop[...]
PDF-Dokument [5.4 MB]
Sodium enhances indium-gallium interdiffusion in copper indium gallium diselenide photovoltaic absorbers
NATURE COMMUNICATIONS (2018) 9:826
Nature Com 2018 interdiffusion in photov[...]
PDF-Dokument [5.6 MB]
Strain-Induced Asymmetric Line Segregation at Faceted Si Grain Boundaries
PHYSICAL REVIEW LETTERS 121, 015702 (2018)
Strain-Induced Asymmetric Line Segregati[...]
PDF-Dokument [1.9 MB]
Linear Complexions: Confined Chemical and Structural States at Dislocations
SCIENCE vol 349: p. 1080, Sept 2015
Dislocations Linear Complexions Science [...]
PDF-Dokument [1.6 MB]
Atomic-scale insights into surface species of electrocatalysts in three dimensions
Nature Catalysis (2018) doi:10.1038/s41929-018-0043-3
Nature Catalysis 2018 - Atomic-scale ins[...]
PDF-Dokument [2.0 MB]
Phase nucleation through confined spinodal fluctuations at crystal defects evidenced in Fe-Mn alloys
Nature Communications
Silva_et_al-2018-Nature_Communications S[...]
PDF-Dokument [2.7 MB]
Ultrastrong steel via minimal lattice misfit and high-density nanoprecipitation
Nature 544, 460–464 (27 April 2017)
Ultrastrong steel via minimal lattice mi[...]
PDF-Dokument [6.2 MB]
Metastable high-entropy dual-phase alloys overcome the strength–ductility trade-off
NATURE | 9 JUNE 2016 | VOL 534 | page 227
Li et al Nature June 2016 Metastable hig[...]
PDF-Dokument [6.0 MB]
Bone-like crack resistance in hierarchical metastable nanolaminate steels
Science 355 (6329), 1055-1057 (10. March 2017)
Koyama Bone-like Steels Science 355 (201[...]
PDF-Dokument [745.8 KB]
Superplasticity in a lean Fe-Mn-Al steel
NATURE COMMUNICATIONS | 8: 751 | DOI: 10.1038/s41467-017-00814-y |
Han_et_al-2017-Nature_Communications Sup[...]
PDF-Dokument [1.1 MB]
A rare-earth free magnesium alloy with improved intrinsic ductility
Scientific Reports 7: 10458 | DOI:10.1038/s41598-017-10384-0
Ductile Magnesium Calcium Aluminium Scie[...]
PDF-Dokument [2.8 MB]
Hydrogen enhances strength and ductility of an equiatomic high entropy alloy
Scientific Reports 7: 9892 | DOI:10.1038/s41598-017-10774-4
Luo et al 2017 Sci Rep Hydrogen strength[...]
PDF-Dokument [2.5 MB]
Complexion-mediated martensitic phasetransformation in Titanium
NATURE COMMUNICATIONS | 8:14210 | DOI: 10.1038/ncomms14210 |www.nature.com/naturecommunications
Zhang_et_al-2017-Nature_Communications.p[...]
PDF-Dokument [6.2 MB]
Core-shell nanoparticle arrays double the strength of steel
Scientific Reports 7:42547 (2017)
Scientific Reports 7 - 42547 Core-shell [...]
PDF-Dokument [2.2 MB]
Atomic-Scale Quantification of Grain Boundary Segregation in Nanocrystalline Material
PHYSICAL REVIEW LETTERS 112, 126103 (2014)
Phys Rev Lett. 2014 grain boundary segre[...]
PDF-Dokument [842.8 KB]

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DAMASK — the Düsseldorf Advanced Material Simulation Kit



DAMASK-OVERVIEW
The solution of a continuum mechanical boundary value problem requires a constitutive response that connects deformation and stress at each material point. Such connection can be regarded as three sep
MPIE-DAMASK-Introduction.pdf
PDF-Dokument [1.4 MB]
DAMASK – The Düsseldorf Advanced Material Simulation Kit for modeling multi-physics crystal plasticity, thermal, and damage phenomena from the single crystal up to the component scale DAMASK – The Düsseldorf Advanced Material Simulation Kit for modeling multi-physics crystal plasticity, thermal, and damage phenomena from the single crystal up to the component scale.
microstructure, TWIP steel, twinning, twins, EBSD, ECCI, steel, alloy design, mechanical properties, tensile test Microstructure scales in TWIP steel (Acta Materialia 59 (2011) 6449)