Capturing 2D van der Waals magnets with high probability for experimental demonstration from materials science literature. Issue 4 (3rd January 2023)
- Record Type:
- Journal Article
- Title:
- Capturing 2D van der Waals magnets with high probability for experimental demonstration from materials science literature. Issue 4 (3rd January 2023)
- Main Title:
- Capturing 2D van der Waals magnets with high probability for experimental demonstration from materials science literature
- Authors:
- Song, Haiyang
Zhao, Yinghe
Turner, Eleanor
Wu, Yu
Li, Yuan
Wu, Menghao
Feng, Guang
Li, Huiqiao
Zhai, Tianyou - Abstract:
- Abstract: 2D van der Waals (vdW) magnets have opened intriguing prospects for next‐generation spintronic nanodevices. Machine learning techniques and density functional theory calculations enable the discovery of 2D vdW magnets to be accelerated; however, current computational frameworks based on these state‐of‐the‐art approaches cannot offer probability analysis on whether a 2D vdW magnet can be experimentally demonstrated. Herein, a new framework can be established to overcome this challenge. Via the framework, 2D vdW magnets with high probability for experimental demonstration are captured from materials science literature. The key to the successful establishment is the introduction of the theory of mutual information. Historical validation of predictions substantiates the high reliability of the framework. For example, half of the 30 2D vdW magnets discovered in the literature published prior to 2017 have been experimentally demonstrated in the subsequent years. This framework has the potential to become a revolutionary force for progressing experimental discovery of 2D vdW magnets. Abstract : This work establishes the first computational framework capable of capturing 2D van der Waals (vdW) magnets with high probability for experimental demonstration. Historical validation of predictions demonstrates its remarkable capacity for accelerating experimental discovery of 2D vdW magnets. Introduction of the theory of mutual information is the key to the resounding success ofAbstract: 2D van der Waals (vdW) magnets have opened intriguing prospects for next‐generation spintronic nanodevices. Machine learning techniques and density functional theory calculations enable the discovery of 2D vdW magnets to be accelerated; however, current computational frameworks based on these state‐of‐the‐art approaches cannot offer probability analysis on whether a 2D vdW magnet can be experimentally demonstrated. Herein, a new framework can be established to overcome this challenge. Via the framework, 2D vdW magnets with high probability for experimental demonstration are captured from materials science literature. The key to the successful establishment is the introduction of the theory of mutual information. Historical validation of predictions substantiates the high reliability of the framework. For example, half of the 30 2D vdW magnets discovered in the literature published prior to 2017 have been experimentally demonstrated in the subsequent years. This framework has the potential to become a revolutionary force for progressing experimental discovery of 2D vdW magnets. Abstract : This work establishes the first computational framework capable of capturing 2D van der Waals (vdW) magnets with high probability for experimental demonstration. Historical validation of predictions demonstrates its remarkable capacity for accelerating experimental discovery of 2D vdW magnets. Introduction of the theory of mutual information is the key to the resounding success of this framework. … (more)
- Is Part Of:
- InfoMat. Volume 5:Issue 4(2023)
- Journal:
- InfoMat
- Issue:
- Volume 5:Issue 4(2023)
- Issue Display:
- Volume 5, Issue 4 (2023)
- Year:
- 2023
- Volume:
- 5
- Issue:
- 4
- Issue Sort Value:
- 2023-0005-0004-0000
- Page Start:
- n/a
- Page End:
- n/a
- Publication Date:
- 2023-01-03
- Subjects:
- 2D vdW magnets -- mutual information -- neural networks
Materials -- Periodicals
Information technology -- Periodicals
Smart materials -- Periodicals
620.11 - Journal URLs:
- http://onlinelibrary.wiley.com/ ↗
https://onlinelibrary.wiley.com/loi/25673165 ↗ - DOI:
- 10.1002/inf2.12397 ↗
- Languages:
- English
- ISSNs:
- 2567-3165
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 27037.xml