A5036281770- High Entropy Alloys Studies
- High-Temperature Coating Behaviors
- Advanced Materials Characterization Techniques
- Additive Manufacturing Materials and Processes
- High Temperature Alloys and Creep
- Advanced materials and composites
- Microstructure and mechanical properties
- Metal and Thin Film Mechanics
- Magnetic Properties of Alloys
- Shape Memory Alloy Transformations
- Machine Learning in Materials Science
- Metallic Glasses and Amorphous Alloys
- Hydrogen embrittlement and corrosion behaviors in metals
- X-ray Diffraction in Crystallography
- Intermetallics and Advanced Alloy Properties
- Nuclear Materials and Properties
- Rare-earth and actinide compounds
- Magnetic and transport properties of perovskites and related materials
- Microstructure and Mechanical Properties of Steels
- Non-Destructive Testing Techniques
- Mineral Processing and Grinding
- Material Properties and Applications
- Advanced ceramic materials synthesis
- Aluminum Alloy Microstructure Properties
- Laser-Ablation Synthesis of Nanoparticles
Max-Planck-Institut für Nachhaltige Materialien
2019-2025
Shanghai Jiao Tong University
2024-2025
Guangzhou University
2021
Max Planck Society
2019
University of Science and Technology Beijing
2016-2017
High-entropy alloys are solid solutions of multiple principal elements that capable 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 fail in high-dimensional spaces. We propose an active learning strategy to accelerate the high-entropy Invar a practically infinite compositional space based very sparse data. Our approach works as...
The lack of strength and damage tolerance can limit the applications conventional soft magnetic materials (SMMs), particularly in mechanically loaded functional devices. Therefore, strengthening toughening SMMs is critically important. However, concepts usually significantly deteriorate properties, due to Bloch wall interactions with defects used for hardening. Here a novel concept overcome this dilemma proposed, by developing bulk excellent mechanical attractive properties through coherent...
Interstitials, e.g., C, N, and O, are attractive alloying elements as small atoms on interstitial sites create strong lattice distortions hence substantially strengthen metals. However, brittle ceramics such oxides carbides usually form, instead of solid solutions, when the content exceeds a critical yet low value (e.g., 2 at.%). Here we introduce class massive solution (MISS) alloys by using highly distorted substitutional host lattice, which enables amounts interstitials an additional...
High-entropy alloys (HEAs) and metallic glasses (MGs) are two material classes based on the massive mixing of multiple-principal elements. HEAs single or multiphase crystalline solid solutions with high ductility. MGs amorphous structure have superior strength but usually poor Here, stacking fault energy in high-entropy nanotwinned phase glass-forming-ability MG same controlled, realizing a novel nanocomposite near theoretical yield (G/24, where G is shear modulus material) homogeneous...
Abstract Since its first emergence in 2004, the high‐entropy alloy (HEA) concept has aimed at stabilizing single‐ or dual‐phase multi‐element solid solutions through high mixing entropy. Here, this strategy is changed and renders such massive metastable, to trigger spinodal decomposition for improving alloys’ magnetic properties. The motivation starting from a HEA approach provide chemical degrees of freedom required tailor behavior using multiple components. key idea form Fe‐Co enriched...
Chemical short-range order (CSRO) refers to atoms of specific elements self-organising within a disordered crystalline matrix form particular atomic neighbourhoods. CSRO is typically characterized indirectly, using volume-averaged or through projection microscopy techniques that fail capture the three-dimensional atomistic architectures. Here, we present machine-learning enhanced approach break inherent resolution limits atom probe tomography enabling imaging multiple CSROs. We showcase our...
Abstract Evaluating and understanding the effect of manufacturing processes on creep performance in superalloys poses a significant challenge due to intricate composition involved. This study presents machine‐learning strategy capable evaluating heat treatment process predicting rupture life with high accuracy. approach integrates classification regression models domain‐specific knowledge. The physical constraints lead significantly enhanced prediction accuracy models. Moreover, is evaluated...
Higher strength and higher ductility are desirable for structural materials. However, ultrastrong alloys inevitably show decreased strain-hardening capacity, limiting their uniform elongation. We present a supranano (<10 nanometers) short-range ordering design grain interiors boundary regions, respectively, in fine-grained based on vanadium, cobalt, nickel, with additions of tungsten, copper, aluminum, boron. The pronounced boundary-related strengthening ductilization mechanism is realized...
Al x Cr 0.4 CuFe MnNi (x = 0, 0.1, 0.2, 0.3, 0.4) high entropy alloys (HEAs) were prepared by copper‐mold casting. The effects of investigated on the microstructure, tensile properties and oxidation behavior these HEAs. It was shown that addition resulted in formation BCC ordered phases. compositional heterogeneity between dendrites interdendrites alleviated when content small. mechanical HEAs have been remarkably improved Al. Considering low cost good properties, this kind is rather...
We combined experimental investigations and theoretical calculations to unveil an abnormal magnetic behavior caused by addition of the nonmagnetic element Cu in face-centered-cubic FeNiCoMn-based high-entropy alloys (HEAs). Upon addition, probed HEAs show increase both Curie temperature saturation magnetization as-cast homogenized states. Specifically, at room increases 77% 177% a content 11 20 at. %, respectively, compared equiatomic FeNiCoMn HEA without Cu. The is associated with formation...
We reveal the impact of magnetic ordering on stacking fault energy (SFE) and its influence deformation mechanisms mechanical properties in a class nonequiatomic quinary Mn-containing compositional complex alloys or high entropy (HEAs). By combining ab initio simulation experimental validation, we demonstrate as an important factor activation transition modes from planar dislocation slip to TWIP (twinning-induced plasticity) and/or TRIP (transformation-induced plasticity). A wide space...
Active learning comprises machine learning-based approaches that integrate surrogate model inference, exploitation and exploration strategies with active experimental feedback into a closed-loop framework. This approach aims at describing predicting specific material properties, without requiring lengthy, expensive or repetitive experiments. Recently, has shown potential as an for the design of sustainable materials, such scrap-compatible alloys, enhancing longevity metallic materials....