Machine learning assisted hierarchical filtering: a strategy for designing magnets with large moment and anisotropy energy

Dutta, Arijit; Sen, Prasenjit

doi:10.1039/d1tc03776e

Cited by 9 publications

(12 citation statements)

References 42 publications

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Section: Algorithms For the 2dfmmentioning

confidence: 99%

“…ML models have been created by Sen et al to efficiently identify and prioritize stable magnetic materials that exhibit significant magnetization and magnetic anisotropy energy through a hierarchical filtering process. [119] As shown in Figure 9(a), out of an initial set of 278 materials, the machine learning models have successfully identified 10 materials that satisfy the desired target properties. A "7-step" hierarchical filtering strategy is employed to screen stable 2D materials with large magnetization and high magnetic anisotropy energy.…”

Section: The ML Prediction Of Magnetic Moments For 2dfmmentioning

confidence: 99%

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Advancements in High‐Throughput Screening and Machine Learning Design for 2D Ferromagnetism: A Comprehensive Review

Xin,

Song,

Jin

et al. 2023

Advcd Theory and Sims

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Abstract2D intrinsic magnetic materials possess unique physical properties distinct from bulk materials, providing an ideal research platform for the development of low‐dimensional spintronics. The traditional approach to developing new materials involves a “trial‐and‐error” method, which is inherently flawed due to long development cycles and high costs. In recent years, with the rapid improvement in computational power, the high throughput (HTP) first‐principles calculation based on density functional theory (DFT) and machine learning (ML) method have provided a highly effective means for the design of novel intrinsic ferromagnetic materials and the study of their magnetic properties. This article reviews the recent research progress in 2D ferromagnetic materials, with particular emphasis on the significant role played by HTP first‐principles calculations and ML in the exploration and fabrication of two‐dimension ferromagnetic (2DFM) materials. Finally, the future development and challenges of 2DFM materials are discussed.

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Section: Algorithms For the 2dfmmentioning

confidence: 99%

Section: The ML Prediction Of Magnetic Moments For 2dfmmentioning

confidence: 99%

Advancements in High‐Throughput Screening and Machine Learning Design for 2D Ferromagnetism: A Comprehensive Review

Xin,

Song,

Jin

et al. 2023

Advcd Theory and Sims

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“…Similar issues with the performance of magnetic moment predictors have been reported in the literature. 29,30,42 Compared to these works, the performance of the TLB-2 is better.…”

mentioning

confidence: 99%

“…The performance is much better than other models for M s prediction. 29,30,42 We also show the parity plots (actual versus predicted property values) of h form and M s predictions in Figure 2a,b. The parity plot of h form demonstrates a balanced prediction over the entire range of values.…”

mentioning

confidence: 99%

“…The design of such materials has been addressed by us and other authors. ,− These works used either high-throughput DFT calculations or ML-assisted screening to identify candidate materials, followed by validation with DFT calculations. So far, generative models have not been employed to address the problem, primarily due to the paucity of materials data for AE.…”

mentioning

confidence: 99%

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MagGen: A Graph-Aided Deep Generative Model for Inverse Design of Permanent Magnets

Mal,

Seal,

Sen

2024

J. Phys. Chem. Lett.

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A deep generative model based on a variational autoencoder (VAE), conditioned simultaneously by two target properties, is developed to inverse design stable magnetic materials. The structure of the physics-informed, property embedded latent space of the model is analyzed using graph theory. An impressive ∼96% of the generated materials are found to satisfy the target properties as per predictions from the target-learning branches. This is a huge improvement over approaches that do not condition the VAE latent space by target properties or that do not consider the connectivity of the parent materials from which the new materials are generated. This impressive feat is achieved by using a simple real-space-only representation that can be directly read from material cif files. Model predictions are finally validated by density functional theory calculations on a randomly chosen subset of materials. The performance of the present model is comparable or superior to that of models reported earlier. This model (MagGen) is applied to the problem of designing rare earth-free permanent magnets with promising results.

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When Machine Learning Meets 2D Materials: A Review

Lu,

Xia,

Ren

et al. 2024

Advanced Science

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The availability of an ever‐expanding portfolio of 2D materials with rich internal degrees of freedom (spin, excitonic, valley, sublattice, and layer pseudospin) together with the unique ability to tailor heterostructures made layer by layer in a precisely chosen stacking sequence and relative crystallographic alignments, offers an unprecedented platform for realizing materials by design. However, the breadth of multi‐dimensional parameter space and massive data sets involved is emblematic of complex, resource‐intensive experimentation, which not only challenges the current state of the art but also renders exhaustive sampling untenable. To this end, machine learning, a very powerful data‐driven approach and subset of artificial intelligence, is a potential game‐changer, enabling a cheaper – yet more efficient – alternative to traditional computational strategies. It is also a new paradigm for autonomous experimentation for accelerated discovery and machine‐assisted design of functional 2D materials and heterostructures. Here, the study reviews the recent progress and challenges of such endeavors, and highlight various emerging opportunities in this frontier research area.

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Machine learning assisted hierarchical filtering: a strategy for designing magnets with large moment and anisotropy energy

Abstract: Machine learning models are developed to hierarchically filter and select stable magnetic mate- rials with large magnetization and magnetic anisotropy energy. Starting from an initial set of 278 materials, 10...

Cited by 9 publications

References 42 publications

Advancements in High‐Throughput Screening and Machine Learning Design for 2D Ferromagnetism: A Comprehensive Review

Advancements in High‐Throughput Screening and Machine Learning Design for 2D Ferromagnetism: A Comprehensive Review

MagGen: A Graph-Aided Deep Generative Model for Inverse Design of Permanent Magnets

When Machine Learning Meets 2D Materials: A Review

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