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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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.

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University of Oxford

in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (1/1 displayed)

  • 2018Crystal nucleation in metallic alloys using x-ray radiography and machine learning96citations

Places of action

Chart of shared publication
Lui, Andrew
1 / 3 shared
Arteta, Carlos
1 / 2 shared
Lempitsky, Victor
1 / 1 shared
Grant, Patrick
1 / 4 shared
Liotti, Enzo
1 / 3 shared
Chart of publication period
2018

Co-Authors (by relevance)

  • Lui, Andrew
  • Arteta, Carlos
  • Lempitsky, Victor
  • Grant, Patrick
  • Liotti, Enzo
OrganizationsLocationPeople

article

Crystal nucleation in metallic alloys using x-ray radiography and machine learning

  • Lui, Andrew
  • Arteta, Carlos
  • Lempitsky, Victor
  • Grant, Patrick
  • Zisserman, Andrew
  • Liotti, Enzo
Abstract

The crystallization of solidifying Al-Cu alloys over a wide range of conditions was studied in situ by synchrotron x-ray radiography, and the data were analyzed using a computer vision algorithm trained using machine learning. The effect of cooling rate and solute concentration on nucleation undercooling, crystal formation rate, and crystal growth rate was measured automatically for thousands of separate crystals, which was impossible to achieve manually. Nucleation undercooling distributions confirmed the efficiency of extrinsic grain refiners and gave support to the widely assumed free growth model of heterogeneous nucleation. We show that crystallization occurred in temporal and spatial bursts associated with a solute-suppressed nucleation zone.

Topics
  • grain
  • crystallization
  • machine learning