Data-driven deep generative design of stable spintronic materials-Reference-Cited by-同舟云学术

Data-driven deep generative design of stable spintronic materials

Published:2023 Issue:43 Volume:25 Page:6017-6029
ISSN:1466-8033
Container-title:CrystEngComm
language:en
Short-container-title:CrystEngComm

Author:

Siriwardane Edirisuriya M. Dilanga¹^ORCID,Zhao Yong²^ORCID,Hu Jianjun²^ORCID

Affiliation:

1. Department of Physics, University of Colombo, Colombo 3, Sri Lanka

2. Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29201, USA

Abstract

A computational framework that integrates generative adversarial networks and machine learning classifiers to enable the discovery of novel magnetic materials.

Funder

National Science Foundation

Publisher

Royal Society of Chemistry (RSC)

Subject

Condensed Matter Physics,General Materials Science,General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2023/CE/D3CE00765K

Reference55 articles.

1. Spintronic devices for energy-efficient data storage and energy harvesting

2. A quantum leap

3. Giant Magnetoresistance: Basic Concepts, Microstructure, Magnetic Interactions and Applications

4. H. A. M.van den Berg , U.Hartmann , R.Coehoorn , M. A. M.Gijs , P.Grünberg , T.Rasing and K.Röll , Magnetic Multilayers and Giant Magnetoresistance: Fundamentals and Industrial Applications , Springer Series in Surface Sciences, Springer Berlin Heidelberg , 2013

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