693.932 PEOPLE
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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Kočí, Jan | Prague |
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| Azam, Siraj |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Gutfleisch, Oliver
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (54/54 displayed)
- 2024Green Ironmaking at Higher H2 Pressure: Reduction Kinetics and Microstructure Formation During Hydrogen-Based Direct Reduction of Hematite Pelletscitations
- 2024Impact of soft magnetic α‐Fe in hard Nd₂Fe₁₄B magnetic materials: A micromagnetic study
- 2024Diffusion behavior of heavy rare-earths for grain boundary engineering of sintered Nd-Fe-B-based permanent magnets produced by the 2-powder methodcitations
- 2024Multifunctional antiperovskites driven by strong magnetostructural coupling
- 2024Exploring the Potential of Nitride and Carbonitride MAX Phases: Synthesis, Magnetic and Electrical Transport Properties of V2GeC, V2GeC0.5N0.5, and V2GeN
- 2024Influence of Colloidal Additivation with Surfactant‐Free Laser‐Generated Metal Nanoparticles on the Microstructure of Suction‐Cast Nd–Fe–B Alloy
- 2024Exploring the Potential of Nitride and Carbonitride MAX Phases: Synthesis, Magnetic and Electrical Transport Properties of V$_2$GeC, V$_2$GeC$_{0.5}$N$_{0.5}$, and V$_2$GeNcitations
- 2023A machine learning framework for quantifying chemical segregation and microstructural features in atom probe tomography data
- 2023Influence of Gd-rich precipitates on the martensitic transformation, magnetocaloric effect, and mechanical properties of Ni–Mn–In Heusler alloys—A comparative studycitations
- 2023Strong and ductile high temperature soft magnets through Widmanstätten precipitates
- 2023Impact of soft magnetic α‐Fe in hard Nd<sub>2</sub>Fe<sub>14</sub>B magnetic materials: A micromagnetic studycitations
- 2023Evaluation of Fe-nitrides, -borides and -carbides for enhanced magnetic fluid hyperthermia with experimental study of α″-Fe<sub>16</sub>N<sub>2</sub> and ϵ-Fe<sub>3</sub>N nanoparticlescitations
- 2023The role of Ni in modifying the order of the phase transition of La(Fe,Ni,Si)(13)citations
- 2023Influence of Colloidal Additivation with Surfactant‐Free Laser‐Generated Metal Nanoparticles on the Microstructure of Suction‐Cast Nd–Fe–B Alloycitations
- 2023Dissipation losses limiting first-order phase transition materials in cryogenic caloric cooling: A case study on all-d-metal Ni(-Co)-Mn-Ti Heusler alloys
- 2023CrB-type, ordered α -MnB: Single crystal structure and spin-canted magnetic behavior
- 2023A Machine Learning Framework for Quantifying Chemical Segregation and Microstructural Features in Atom Probe Tomography Datacitations
- 2023Designing magnetocaloric materials for hydrogen liquefaction with light rare-earth Laves phasescitations
- 2022A mechanically strong and ductile soft magnet with extremely low coercivitycitations
- 2022Exploring V-Fe-Co-Ni-Al and V-Fe-Co-Ni-Cu high entropy alloys for magnetocaloric applications ; ENEngelskEnglishExploring V-Fe-Co-Ni-Al and V-Fe-Co-Ni-Cu high entropy alloys for magnetocaloric applicationscitations
- 2022Exploring V-Fe-Co-Ni-Al and V-Fe-Co-Ni-Cu high entropy alloys for magnetocaloric applicationscitations
- 2022Machine learning–enabled high-entropy alloy discoverycitations
- 2022A Novel Magnetic Hardening Mechanism for Nd‐Fe‐B Permanent Magnets Based on Solid‐State Phase Transformation
- 2021Multifunctional antiperovskites driven by strong magnetostructural couplingcitations
- 2021A two-sublattice model for extracting rare-earth anisotropy constants from measurements on (Nd,Ce)2(Fe,Co)14B single crystalscitations
- 2021Design and Qualification of Pr-Fe-Cu-B Alloys for the Additive Manufacturing of Permanent Magnetscitations
- 2021Alloying effect on the order-disorder transformation in tetragonal FeNicitations
- 2021Twins - A weak link in the magnetic hardening of ThMn12-type permanent magnetscitations
- 2021Intrinsically weak magnetic anisotropy of cerium in potential hard-magnetic intermetallicscitations
- 2021Neutron study of magnetic correlations in rare-earth-free Mn-Bi magnetscitations
- 2020Unveiling the mechanism of abnormal magnetic behavior of FeNiCoMnCu high-entropy alloys through a joint experimental-theoretical studycitations
- 2020HDDR treatment of Ce-substituted Nd2Fe14B-based permanent magnet alloys - phase structure evolution, intergranular processes and magnetic property developmentcitations
- 2020Tuning the magnetocrystalline anisotropy of Fe3Sn by alloyingcitations
- 2019The role of Ni in modifying the order of the phase transition of La(Fe,Ni,Si)13citations
- 2019Critical raw materials - Advanced recycling technologies and processes: Recycling of rare earth metals out of end of life magnets by bioleaching with various bacteria as an example of an intelligent recycling strategycitations
- 2019Towards manufacturing of Nd-Fe-B magnets by continuous rotary swaging of cast alloycitations
- 2019Ce and La as substitutes for Nd in Nd2Fe14B-based melt-spun alloys and hot-deformed magnets: A comparison of structural and magnetic propertiescitations
- 2019High-throughput screening of rare-earth-lean intermetallic 1-13-X compounds for good hard-magnetic propertiescitations
- 2019Experimental and computational analysis of binary Fe-Sn ferromagnetic compoundscitations
- 2018Anisotropic local hardening in hot-deformed Nd-Fe-B permanent magnetscitations
- 2018A Comparative Study on the Magnetocaloric Properties of Ni-Mn-X(-Co) Heusler Alloyscitations
- 2017Heat exchangers from metal-bonded La(Fe,Mn,Si)13Hx powdercitations
- 2016On the S(T) diagram of magnetocaloric materials with first-order transition: Kinetic and cyclic effects of Heusler alloyscitations
- 2016REE Recovery from End-of-Life NdFeB Permanent Magnet Scrap: A Critical Reviewcitations
- 2016A unified approach to describe the thermal and magnetic hysteresis in Heusler alloyscitations
- 2014Interface effects in NaAlH4-carbon nanocomposites for hydrogen storage
- 2014High hydrogen content super-lightweight intermetallics from the Li–Mg–Si systemcitations
- 2014Unusual oxidation behavior of light metal hydride by tetrahydrofuran solvent molecules confined in ordered mesoporous carboncitations
- 2014Unusual oxidation behavior of light metal hydride by tetrahydrofuran solvent molecules confined in ordered mesoporous carboncitations
- 2013Chemical state, distribution and role of Ti- and Nb-based additives on the Ca(BH4)2 systemcitations
- 2009Phase transformations and magnetic structure of nanocrystalline Fe-Pd and Co-Pt alloys studied by in situ neutron powder diffractioncitations
- 2007Hydrogen sorption properties of MgH 2 -LiBH 4 compositescitations
- 2007Hydrogen sorption properties of MgH2-LiBH4 compositescitations
- 2003Hydrogenation properties of nanocrystalline Mg- and Mg₂Ni-based compounds modified with platinum group metals (PGMs)
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article
A Machine Learning Framework for Quantifying Chemical Segregation and Microstructural Features in Atom Probe Tomography Data
Abstract
<jats:title>Abstract</jats:title><jats:p>Atom probe tomography (APT) is ideally suited to characterize and understand the interplay of segregation and microstructure in modern multi-component materials. Yet, the quantitative analysis typically relies on human expertise to define regions of interest. We introduce a computationally efficient, multi-stage machine learning strategy to identify compositionally distinct domains in a semi-automated way, and subsequently quantify their geometric and compositional characteristics. In our algorithmic pipeline, we first coarse-grain the APT data into voxels, collect the composition statistics, and decompose it via clustering in composition space. The composition classification then enables the real-space segmentation via a density-based clustering algorithm, thus revealing the microstructure at voxel resolution. Our approach is demonstrated for a Sm–(Co,Fe)–Zr–Cu alloy. The alloy exhibits two precipitate phases with a plate-like, but intertwined morphology. The primary segmentation is further refined to disentangle these geometrically complex precipitates into individual plate-like parts by an unsupervised approach based on principle component analysis, or a U-Net-based semantic segmentation trained on the former. Following the composition and geometric analysis, detailed composition distribution and segregation effects relative to the predominant plate-like geometry can be readily mapped from the point cloud, without resorting to the voxel compositions.</jats:p>