Data-driven optimization of FePt heat-assisted magnetic recording media accelerated by deep learning TEM image segmentation

Кулеш, Н. А.; Bolyachkin, A.; Suzuki, Itsuko S.; Takahashi, Y. K.; Sepehri-Amin, H.; Hono, K.

doi:10.1016/j.actamat.2023.119039

Acta Materialia

2023

DOI: 10.1016/j.actamat.2023.119039

|View full text |Cite

Data-driven optimization of FePt heat-assisted magnetic recording media accelerated by deep learning TEM image segmentation

Н. А. Кулеш

A. Bolyachkin

Itsuko S. Suzuki

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

2025

Publication Types

Select...

Article5

Relationship

Self Cite0

Independent5

Authors

Journals

Cited by 6 publications

(1 citation statement)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The rapid development of high‐throughput computing, experimental characterization, sensor technology, image recognition, and text mining has significantly improved the efficiency of data accumulation. The rapid accumulation of multi‐dimensional and multi‐modal data such as materials surface and interface morphology, [20] mechanical properties, [21] catalytic properties, [22] semiconductor properties, [23] and service properties [24] has greatly expanded the scale of data resources. This has further led to the establishment of a range of high‐quality databases and underpins the construction of multiple key materials data platforms [25–28] .…”

mentioning

confidence: 99%

Prospects of materials genome engineering frontiers

Xie

2023

Materials Genome Engineering Advances

View full text Add to dashboard Cite

Materials genome engineering represents the new frontier of materials research, and is disrupting the conventional “trial and error” paradigm for materials innovation. In the present perspective, the author reflects on the major achievements already made in five sub‐domains, including high‐efficiency materials computation and design, revolutionary experimental technologies, materials big data technologies, research and development of advanced materials, and industrial applications. Furthermore, the author lays out five crucial directions of future efforts for maturing the relevant technologies. These directions include cross‐scale modeling and computational design, artificial intelligence for materials science, automatic and intelligent experimentation, digital twin, and data resource management and sharing.

show abstract

mentioning

confidence: 99%

Prospects of materials genome engineering frontiers

Xie

2023

Materials Genome Engineering Advances

View full text Add to dashboard Cite

show abstract

Data-driven compositional optimization of La(Fe,Si)13-based magnetocaloric compounds for cryogenic applications

Srinithi,

Bolyachkin,

Tang

et al. 2025

Scripta Materialia

View full text Add to dashboard Cite

Atomic layer deposition of magnetic thin films: Basic processes, engineering efforts, and road forward

Jussila,

Philip,

Tripathi

et al. 2023

Applied Physics Reviews

View full text Add to dashboard Cite

Atomic layer deposition (ALD) is known as a key enabler of the continuous advances in device engineering for microelectronics. For instance, the state-of-the-art transistor technology depends entirely on ALD-grown high-κ materials. Another application branch where ALD could potentially play a similar important role in future is the magnetic thin film devices. Spin-based devices based on high-quality magnetic thin films are anticipated to provide high-efficiency operations with low power consumption. The strict quality demands the magnetic thin films must fulfill in the next-generation applications form the strong bases for the efforts to implement ALD in this application area. In this first comprehensive review on the topic, our aim is to provide an insightful account of the ALD processes so far developed for magnetic materials and to highlight the application-relevant magnetic properties of the thus fabricated thin films. Moreover, we discuss the various innovative engineering efforts made toward different multi-layered and nanostructured composite materials and complex architectures uniquely enabled by the sophisticated self-terminated film-growth mechanism of ALD. The review is finished with a brief outlook toward the future prospects and challenges in the field.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Data-driven optimization of FePt heat-assisted magnetic recording media accelerated by deep learning TEM image segmentation

Cited by 6 publications

References 26 publications

Prospects of materials genome engineering frontiers

Prospects of materials genome engineering frontiers

Data-driven compositional optimization of La(Fe,Si)13-based magnetocaloric compounds for cryogenic applications

Atomic layer deposition of magnetic thin films: Basic processes, engineering efforts, and road forward

Contact Info

Product

Resources

About