Rapid screening of high-entropy alloys using neural networks and constituent elements

Nassar, AE and Mullis, AM orcid.org/0000-0002-5215-9959 (2021) Rapid screening of high-entropy alloys using neural networks and constituent elements. Computational Materials Science, 199. 110755. ISSN 0927-0256

Abstract

Metadata

Authors/Creators:
Copyright, Publisher and Additional Information: Crown Copyright © 2021 Published by Elsevier B.V. All rights reserved. This is an author produced version of an article published in Computational Materials Science. Uploaded in accordance with the publisher's self-archiving policy.
Keywords: High-entropy alloys; Multi-principal element alloys; Neural network; Phase prediction; Solidification microstructure
Dates:
  • Published: November 2021
  • Accepted: 26 July 2021
  • Published (online): 2 August 2021
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Chemical & Process Engineering (Leeds)
Funding Information:
FunderGrant number
EPSRC (Engineering and Physical Sciences Research Council)EP/N007638/1
Depositing User: Symplectic Publications
Date Deposited: 18 Aug 2021 14:53
Last Modified: 18 Aug 2021 14:53
Status: Published
Publisher: Elsevier
Identification Number: https://doi.org/10.1016/j.commatsci.2021.110755

Download

Accepted Version


Embargoed until: 2 August 2022

Filename: Neural Networks - Nassar&Mullis.pdf

Licence: CC-BY-NC-ND 4.0

Request a copy

file not available

Share / Export

Statistics