2-Dimensional magnetic materials for spintronics technology

Awan, Saif Ullah; Zainab, Sana; Khan, Malik Dilshad; Rizwan, Syed; Iqbal, Muhammad Zahir

doi:10.1039/9781788017053-00091

Cited by 2 publications

(1 citation statement)

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“…Two-dimensional magnetic materials have attracted significant attention due to their potential applications in spintronics and data storage [171][172][173][174][175][176][177]. Stimulated by these exciting experimental reports, 56 new magnetically ordered monolayer structures were predicted from high-throughput computation to be exfoliated from known magnetic bulk materials [113].…”

Section: Evolutionary and Global Optimizationmentioning

confidence: 99%

Review on automated 2D material design

Al-Maeeni,

Lazarev,

Kazeev

et al. 2024

2D Mater.

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Deep learning (DL) methodologies have led to significant advancements in various domains, facilitating intricate data analysis and enhancing predictive accuracy and data generation quality through complex algorithms. In materials science, the extensive computational demands associated with high-throughput screening (HTS) techniques such as Density Functional Theory (DFT), coupled with limitations in laboratory production, present substantial challenges for material research. DL techniques are poised to alleviate these challenges by reducing the computational costs of simulating material properties and by generating novel materials with desired attributes. This comprehensive review paper delves into the current state of DL applications in materials design, with a particular emphasis on two-dimensional materials. The article encompasses an in-depth exploration of data-driven approaches in both forward and inverse design within the realm of materials science.

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