Materials Map

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

  • About
  • Privacy Policy
  • Legal Notice
  • Contact

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.

×

Materials Map under construction

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.

To Graph

1.080 Topics available

To Map

977 Locations available

693.932 PEOPLE
693.932 People People

693.932 People

Show results for 693.932 people that are selected by your search filters.

←

Page 1 of 27758

→
←

Page 1 of 0

→
PeopleLocationsStatistics
Naji, M.
  • 2
  • 13
  • 3
  • 2025
Motta, Antonella
  • 8
  • 52
  • 159
  • 2025
Aletan, Dirar
  • 1
  • 1
  • 0
  • 2025
Mohamed, Tarek
  • 1
  • 7
  • 2
  • 2025
Ertürk, Emre
  • 2
  • 3
  • 0
  • 2025
Taccardi, Nicola
  • 9
  • 81
  • 75
  • 2025
Kononenko, Denys
  • 1
  • 8
  • 2
  • 2025
Petrov, R. H.Madrid
  • 46
  • 125
  • 1k
  • 2025
Alshaaer, MazenBrussels
  • 17
  • 31
  • 172
  • 2025
Bih, L.
  • 15
  • 44
  • 145
  • 2025
Casati, R.
  • 31
  • 86
  • 661
  • 2025
Muller, Hermance
  • 1
  • 11
  • 0
  • 2025
Kočí, JanPrague
  • 28
  • 34
  • 209
  • 2025
Šuljagić, Marija
  • 10
  • 33
  • 43
  • 2025
Kalteremidou, Kalliopi-ArtemiBrussels
  • 14
  • 22
  • 158
  • 2025
Azam, Siraj
  • 1
  • 3
  • 2
  • 2025
Ospanova, Alyiya
  • 1
  • 6
  • 0
  • 2025
Blanpain, Bart
  • 568
  • 653
  • 13k
  • 2025
Ali, M. A.
  • 7
  • 75
  • 187
  • 2025
Popa, V.
  • 5
  • 12
  • 45
  • 2025
Rančić, M.
  • 2
  • 13
  • 0
  • 2025
Ollier, Nadège
  • 28
  • 75
  • 239
  • 2025
Azevedo, Nuno Monteiro
  • 4
  • 8
  • 25
  • 2025
Landes, Michael
  • 1
  • 9
  • 2
  • 2025
Rignanese, Gian-Marco
  • 15
  • 98
  • 805
  • 2025

Bernal, Miguel

  • Google
  • 3
  • 13
  • 19

in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (3/3 displayed)

  • 2024A microscopic view on the electrochemical deposition and dissolution of Au with scanning electrochemical cell microscopy – Part II4citations
  • 2023Estimating pitting descriptors of 316 L stainless steel by machine learning and statistical analysis15citations
  • 2023Estimating pitting descriptors of 316L stainless steel by machine learning and statistical analysiscitations

Places of action

Chart of shared publication
Parapari, Sorour Semsari
1 / 3 shared
Torres, Daniel
2 / 3 shared
Žužek, Kristina
1 / 1 shared
Ustarroz, Jon
3 / 15 shared
Šturm, Sašo
1 / 14 shared
Coelho, Leonardo Bertolucci
3 / 5 shared
Delfosse, Suzanne
1 / 1 shared
Čeh, Miran
1 / 4 shared
Morillo, Daniel Torres
1 / 1 shared
Bontempi, Gianluca
2 / 2 shared
Vangrunderbeek, Vincent
2 / 2 shared
Paldino, Gian Marco
1 / 1 shared
Paldino, Gian
1 / 1 shared
Chart of publication period
2024
2023

Co-Authors (by relevance)

  • Parapari, Sorour Semsari
  • Torres, Daniel
  • Žužek, Kristina
  • Ustarroz, Jon
  • Šturm, Sašo
  • Coelho, Leonardo Bertolucci
  • Delfosse, Suzanne
  • Čeh, Miran
  • Morillo, Daniel Torres
  • Bontempi, Gianluca
  • Vangrunderbeek, Vincent
  • Paldino, Gian Marco
  • Paldino, Gian
OrganizationsLocationPeople

document

Estimating pitting descriptors of 316L stainless steel by machine learning and statistical analysis

  • Bernal, Miguel
  • Torres, Daniel
  • Bontempi, Gianluca
  • Paldino, Gian
  • Vangrunderbeek, Vincent
  • Ustarroz, Jon
  • Coelho, Leonardo Bertolucci
Abstract

<jats:title>Abstract</jats:title><jats:p>A hybrid rule-base/ML approach using linear regression and artificial neural networks (ANN) determined pitting corrosion descriptors from high-throughput data obtained with Scanning Electrochemical Cell Microscopy (SECCM) on 316L stainless steel. Non-parametric density estimation determined the central tendencies of the E<jats:italic>pit</jats:italic>/log(<jats:italic>jpit</jats:italic>) and E<jats:italic>pass</jats:italic>/log(<jats:italic>jpass</jats:italic>) distributions. Descriptors estimated using conditional mean or median curves were compared to their central tendency values, with the conditional medians providing more accurate results. Due to their lower sensitivity to high outliers, the conditional medians were more robust representations of the log(<jats:italic>j</jats:italic>) Vs <jats:italic>E</jats:italic> distributions. An observed trend of passive range shortening with increasing testing aggressiveness was attributed to delayed stabilisation of the passive film, rather than early passivity breakdown.</jats:p>

Topics
  • density
  • stainless steel
  • pitting corrosion
  • positron annihilation lifetime spectroscopy
  • Photoacoustic spectroscopy
  • machine learning
  • microscopy