2017
DOI: 10.1038/s41524-017-0020-4
|View full text |Cite
|
Sign up to set email alerts
|

Understanding and designing magnetoelectric heterostructures guided by computation: progresses, remaining questions, and perspectives

Abstract: Magnetoelectric composites and heterostructures integrate magnetic and dielectric materials to produce new functionalities, e.g., magnetoelectric responses that are absent in each of the constituent materials but emerge through the coupling between magnetic order in the magnetic material and electric order in the dielectric material. The magnetoelectric coupling in these composites and heterostructures is typically achieved through the exchange of magnetic, electric, or/and elastic energy across the interfaces… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1

Citation Types

1
90
0

Year Published

2017
2017
2023
2023

Publication Types

Select...
8
1

Relationship

1
8

Authors

Journals

citations
Cited by 126 publications
(93 citation statements)
references
References 243 publications
(275 reference statements)
1
90
0
Order By: Relevance
“…A more intricate method employing a gradient boosting decision tree and universal fragment descriptors is able to predict the electronic as well as mechanical properties, such as bulk and shear modulus, heat capacity, and Debye temperature 25 . Machine learning has also allowed researchers to predict glass formation 26 , study magnetoelectric heterostructures 27 , and optimize microstructure 28,29 . The great advantage of using statistical machine-learning methods is that predictions can be quickly made for any given combination of elements, any stoichiometry, or any size unit cell.…”
Section: Introductionmentioning
confidence: 99%
“…A more intricate method employing a gradient boosting decision tree and universal fragment descriptors is able to predict the electronic as well as mechanical properties, such as bulk and shear modulus, heat capacity, and Debye temperature 25 . Machine learning has also allowed researchers to predict glass formation 26 , study magnetoelectric heterostructures 27 , and optimize microstructure 28,29 . The great advantage of using statistical machine-learning methods is that predictions can be quickly made for any given combination of elements, any stoichiometry, or any size unit cell.…”
Section: Introductionmentioning
confidence: 99%
“…Magnetoelectric coupling in magnetic/dielectric heterostructures enables ultralow-power spintronic devices by using a non-powerdissipating electric field to control the directions and/or the transport of spins. [1][2][3] One well-pursued scheme is to use an electric field to reverse the magnetization, suggesting applications in magnetic memories 2 and logic gates. 4 However, this is challenging because a time-invariant electric field cannot break the time-reversal symmetry of magnetization.…”
Section: Introductionmentioning
confidence: 99%
“…The values of these unknown features can be obtained by solving the system of equations (Eqs. [2][3][4][5][6].…”
Section: Methodsmentioning
confidence: 99%
“…D ata-driven methods such as machine learning (ML) and statistical analysis (SA) are efficient toolsets for extracting process-structure-property relation or for designsynthesis-characterization of materials [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18] . ML and SA are able to address large and complex tasks by focusing on the most relevant information in an overwhelming quantity of data while providing similar or better accuracy to the finite element analysis (FEA) and experiment [19][20][21][22] .…”
mentioning
confidence: 99%