Artificial Intelligence Guided Studies of van der Waals Magnets

Rhone, Trevor David; Bhattarai, Romakanta; Gavras, Haralambos; Lusch, Bethany; Salim, Misha; Mattheakis, Marios; Larson, D. J.; Krockenberger, Yoshiharu; Kaxiras, Efthimios

doi:10.1002/adts.202300019

Cited by 8 publications

(1 citation statement)

References 59 publications

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“…Additionally, a semi-supervised ML approach was adopted to identify thermodynamically stable 2D magnets for spintronic applications . The investigation of 2D magnets by means of atomic substitution has been carried out for compounds of the forms A 2 B 2 Te 6 and A i A ii B 4 X 8 using DFT and ML. , ML approaches such as XGBoost has also proven effective in gaining a deeper physical understanding of the magnetic ordering of 2D magnets. , …”

Section: Introductionmentioning

confidence: 99%

Accelerated Data-Driven Discovery and Screening of Two-Dimensional Magnets Using Graph Neural Networks

Elrashidy,

Della-Giustina,

Yan

2024

J. Phys. Chem. C

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In this study, we employ graph neural networks (GNNs) to accelerate the discovery of novel 2D magnetic materials which have transformative potential in spintronic applications. Using data from the Materials Project database and the Computational 2D materials database, we train three GNN architectures on a dataset of 1190 magnetic monolayers with energy above the convex hull (E hull ) less than 0.3 eV/atom. Our Crystal Diffusion Variational Autoencoder generates 11,100 candidate crystals. Subsequent training on two Atomistic Line GNNs achieves 93% accuracy in predicting magnetic monolayers and a mean average error of 0.039 eV/atom for E hull predictions. After narrowing down candidates based on magnetic likelihood and predicted energy, constraining the atom count in the monolayers to five or fewer, and performing dimensionality checks, we identified 190 candidates. These are validated using density-functional theory to confirm their magnetic and energetic favorability, resulting in 167 magnetic monolayers with E hull < 0.3 eV/atom and a total magnetization of ≥0.5 μ B . Our methodology offers a way to accelerate the exploration and prediction of potential 2D magnetic materials, contributing to the ongoing computational and experimental efforts aimed at the discovery of new 2D magnets.

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Section: Introductionmentioning

confidence: 99%

Accelerated Data-Driven Discovery and Screening of Two-Dimensional Magnets Using Graph Neural Networks

Elrashidy,

Della-Giustina,

Yan

2024

J. Phys. Chem. C

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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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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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Artificial Intelligence Guided Studies of van der Waals Magnets

Cited by 8 publications

References 59 publications

Accelerated Data-Driven Discovery and Screening of Two-Dimensional Magnets Using Graph Neural Networks

Accelerated Data-Driven Discovery and Screening of Two-Dimensional Magnets Using Graph Neural Networks

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

When Machine Learning Meets 2D Materials: A Review

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