Materials Map

Discover the materials research landscape. Find experts, partners, networks.

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The Materials Map is an open tool for improving networking and interdisciplinary exchange within materials research. It enables cross-database search for cooperation and network partners and discovering of the research landscape.

The dashboard provides detailed information about the selected scientist, e.g. publications. The dashboard can be filtered and shows the relationship to co-authors in different diagrams. In addition, a link is provided to find contact information.

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The Materials Map is still under development. In its current state, it is only based on one single data source and, thus, incomplete and contains duplicates. We are working on incorporating new open data sources like ORCID to improve the quality and the timeliness of our data. We will update Materials Map as soon as possible and kindly ask for your patience.

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in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (9/9 displayed)

  • 2022A Data-Driven Approach for Studying the Influence of Carbides on Work Hardening of Steel3citations
  • 2022A Data-Driven Approach for Studying the Influence of Carbides on Work Hardening of Steel3citations
  • 2022Isotonic regression for metallic microstructure data: estimation and testing under order restrictions4citations
  • 2022Role of planar faults in martensite formation in nano-polycrystalline iron by molecular dynamics simulation4citations
  • 2021The Role of Dimensionality on the Optoelectronic Properties of Oxide and Halide Perovskites, and their Halide Derivativescitations
  • 2021Isotonic regression for metallic microstructure data4citations
  • 2020The Doping Mechanism of Halide Perovskite Unveiled by Alkaline Earth Metals166citations
  • 2020Influence of M23C6 carbides on the heterogeneous strain development in annealed 420 stainless steel46citations
  • 2014Mechanical and functional properties of Invar alloy for µ-MIMcitations

Places of action

Chart of shared publication
Jongbloed, Geurt
2 / 6 shared
Sietsma, Jilt
3 / 44 shared
Vittorietti, Martina
3 / 8 shared
Lopez, J. Galan
1 / 1 shared
Vittorietti, M.
2 / 6 shared
Sietsma, J.
2 / 96 shared
Jongbloed, G.
2 / 5 shared
Galan Lopez, J.
1 / 7 shared
Li, W.
1 / 48 shared
Jagt, Ra
1 / 2 shared
Macmanus-Driscoll, Jl
1 / 16 shared
Fix, T.
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Hoye, Rlz
1 / 14 shared
Correa-Baena, Jp
1 / 2 shared
Li, Wei
1 / 6 shared
Farahani, Hussein
1 / 2 shared
Petrov, Roumen
1 / 71 shared
Vercruysse, F.
1 / 4 shared
Jimenez-Morales, A.
1 / 2 shared
Torralba, José Manuel
1 / 9 shared
Gelin, Jean
1 / 15 shared
Barriere, Thierry
1 / 45 shared
Chart of publication period
2022
2021
2020
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Co-Authors (by relevance)

  • Jongbloed, Geurt
  • Sietsma, Jilt
  • Vittorietti, Martina
  • Lopez, J. Galan
  • Vittorietti, M.
  • Sietsma, J.
  • Jongbloed, G.
  • Galan Lopez, J.
  • Li, W.
  • Jagt, Ra
  • Macmanus-Driscoll, Jl
  • Fix, T.
  • Hoye, Rlz
  • Correa-Baena, Jp
  • Li, Wei
  • Farahani, Hussein
  • Petrov, Roumen
  • Vercruysse, F.
  • Jimenez-Morales, A.
  • Torralba, José Manuel
  • Gelin, Jean
  • Barriere, Thierry
OrganizationsLocationPeople

article

Isotonic regression for metallic microstructure data

  • Jongbloed, Geurt
  • Sietsma, Jilt
  • Vittorietti, Martina
  • Hidalgo, J.
  • Li, Wei
Abstract

<p>Investigating the main determinants of the mechanical performance of metals is not a simple task. Already known physically inspired qualitative relations between 2D microstructure characteristics and 3D mechanical properties can act as the starting point of the investigation. Isotonic regression allows to take into account ordering relations and leads to more efficient and accurate results when the underlying assumptions actually hold. The main goal in this paper is to test order relations in a model inspired by a materials science application. The statistical estimation procedure is described considering three different scenarios according to the knowledge of the variances: known variance ratio, completely unknown variances, and variances under order restrictions. New likelihood ratio tests are developed in the last two cases. Both parametric and non-parametric bootstrap approaches are developed for finding the distribution of the test statistics under the null hypothesis. Finally an application on the relation between geometrically necessary dislocations and number of observed microstructure precipitations is shown.</p>

Topics
  • impedance spectroscopy
  • microstructure
  • dislocation
  • precipitation