Combinatorial Survey of Structural Phase Distribution and Magnetism in Fe-Ge-Te Composition-spread Thin Film Libraries

Chih-Yu Lee; Efrain E. Rodriguez; Ichiro Takeuchi; Joseph W. Bennett; Masato Kotsugi; Peng Yan; Ryan Kim; Takahiro Yamazaki

arxiv: 2605.18037 · v1 · pith:5TDABCGHnew · submitted 2026-05-18 · ❄️ cond-mat.mtrl-sci

Combinatorial Survey of Structural Phase Distribution and Magnetism in Fe-Ge-Te Composition-spread Thin Film Libraries

Chih-Yu Lee , Takahiro Yamazaki , Peng Yan , Ryan Kim , Masato Kotsugi , Efrain E. Rodriguez , Joseph W. Bennett , Ichiro Takeuchi This is my paper

Pith reviewed 2026-05-20 09:47 UTC · model grok-4.3

classification ❄️ cond-mat.mtrl-sci

keywords Fe-Ge-Tecombinatorial thin filmsferromagnetismstructural phasesmachine learning clusteringX-ray diffractiondensity functional theory

0 comments

The pith

Hexagonal crystal structure is required for ferromagnetism in Fe-Ge-Te thin films

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The study maps structural phases and magnetic behavior across a wide range of iron-germanium-tellurium compositions in thin-film form. Researchers prepared a composition-spread library by co-sputtering and annealing, then measured crystal structure on 177 pads using X-ray diffraction and applied unsupervised machine learning to group pads with matching diffraction patterns. Magnetic measurements on the resulting groups showed ferromagnetism only where the patterns indicated a hexagonal arrangement of atoms. Density-functional calculations of magnetization and structure aligned with the experimental pattern. The work therefore treats the hexagonal arrangement as the necessary condition that turns on magnetism and uses the combinatorial plus clustering workflow to flag other compositions likely to share that property.

Core claim

The hexagonal crystal structure is a critical prerequisite for ferromagnetism in this system, and unexplored materials adopting this structure can be efficiently identified as possible ferromagnetic materials using a high-throughput, ML-assisted framework based on combinatorial thin-film libraries.

What carries the argument

Unsupervised machine-learning clustering of X-ray diffraction patterns that groups compositions sharing the same structural phase, then correlates those groups with magnetometry and XMCD data plus DFT magnetization models.

If this is right

Only hexagonal-phase compositions in the Fe-Ge-Te library exhibit ferromagnetic ordering.
The combinatorial workflow produces a composition-structure-magnetism map across a broad range without preparing one sample at a time.
Density-functional theory models reproduce the observed link between hexagonal structure and magnetism.
New candidate ferromagnetic materials can be screened simply by checking whether they adopt the hexagonal structure.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same combinatorial-plus-clustering method could be applied to other two-dimensional van-der-Waals magnet families to locate additional room-temperature candidates.
Extending the library to include post-annealing or substrate variations might reveal how to stabilize the hexagonal phase in compositions that normally do not form it.
Adding electrical-transport or optical measurements to the same pads would test whether the structure-magnetism link also governs other functional properties.

Load-bearing premise

The machine-learning clusters of X-ray diffraction patterns correctly identify distinct crystal structures without mislabeling, and the density-functional calculations correctly predict which structures carry magnetism.

What would settle it

Detection of clear ferromagnetism in a composition whose diffraction pattern falls into a non-hexagonal cluster, or complete absence of magnetism in a composition confirmed to be hexagonal.

read the original abstract

Recently, magnetic 2-dimensional (2D) van der Waals (vdW) materials have garnered tremendous attention. The vdW ferromagnet Fe5Ge1Te2 has a Curie temperature Tc of ~ 270 K, which is tailorable by tuning the stoichiometry and the Fe deficiency to reach room temperature. To explore the expanded compositional space, we implemented combinatorial synthesis and high-throughput characterization to investigate the structural phase distribution and ferromagnetism of a Fe-Ge-Te thin film library. The library was prepared by magnetron co-sputtering followed by annealing in vacuum or in an inert environment. Composition and structural phase distribution of the 177 pads in the library were characterized using high-throughput wavelength dispersive spectroscopy (WDS), X-ray diffraction (XRD), and two-point probe resistance measurements. We leverage unsupervised machine learning to cluster the XRD dataset into groups of compositions with similar structural phases, and further study the ferromagnetic properties via SQUID magnetometry and X-ray magnetic circular dichroism (XMCD) across different clusters. The results are compared against magnetization and structural models calculated using DFT. Our results demonstrate that the hexagonal crystal structure is a critical prerequisite for ferromagnetism in this system, and that unexplored materials adopting this structure can be efficiently identified as possible ferromagnetic materials using our high-throughput, ML-assisted framework. This workflow based on the combinatorial strategy allows us to rapidly capture the composition-structure-magnetic property map across a broad compositional landscape of novel magnetic materials.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper delivers a useful high-throughput map of Fe-Ge-Te phases and magnetism via ML-clustered XRD but the claim that hexagonal structure is a strict prerequisite rests on clustering that may not fully separate real phases from thin-film artifacts.

read the letter

The main thing to know is that the authors made a 177-pad Fe-Ge-Te composition-spread library by co-sputtering, annealed the films, and used unsupervised ML to group the XRD patterns into structural clusters. They then measured magnetism with SQUID and XMCD on those clusters and compared the results to DFT models, concluding that only the hexagonal phase shows ferromagnetism and that this ML workflow can flag other candidates quickly. That combination of scale and automated phase grouping is the concrete advance here. The experimental throughput is solid: wavelength-dispersive spectroscopy for composition, two-point resistance, and the direct link from clusters to magnetic data give a practical composition-structure-property picture that prior single-composition studies on Fe5GeTe2 did not have. Credit the authors for shipping the full library data and for testing both vacuum and inert annealing conditions. The soft spot is exactly the one the stress-test flags. Thin-film XRD is prone to preferred orientation, strain broadening, and overlapping peaks from related Fe-Ge-Te phases, so k-means or PCA-based clustering on raw patterns can mix dissimilar structures or create spurious groups. The abstract and available description give no clear validation metrics, cross-checks against known standards, or error bars on the cluster assignments, and the DFT comparisons assume bulk, perfect-stoichiometry cells that may not match the annealed sputtered films. If even a few pads in the non-hexagonal clusters actually contain hexagonal material or if magnetism appears outside the main hexagonal cluster, the prerequisite inference weakens. This work is for people doing combinatorial screening of 2D magnets or high-throughput structural mapping. Readers who need a concrete example of ML-assisted phase identification in a real material library will get value from the workflow and the data volume. It has enough experimental substance and a clear question to deserve a serious referee, though the review should focus on the robustness of the clustering and any direct evidence that magnetism is absent outside the hexagonal group. I would send it out for review with a request for those validation details.

Referee Report

2 major / 2 minor

Summary. The manuscript reports a high-throughput combinatorial study of a 177-pad Fe-Ge-Te thin-film library synthesized by magnetron co-sputtering and annealing. Composition is mapped via wavelength-dispersive spectroscopy, structural phases via X-ray diffraction, and transport via two-point resistance. Unsupervised machine learning clusters the XRD patterns into groups presumed to share similar crystal structures. Ferromagnetism is then probed by SQUID magnetometry and XMCD across clusters and compared to DFT magnetization and structure calculations. The central claim is that the hexagonal phase is a necessary prerequisite for ferromagnetism in this system and that the ML-assisted workflow can efficiently flag unexplored compositions adopting this structure as candidate ferromagnets.

Significance. If the phase-magnetism correlation is robust, the work supplies a practical, scalable template for exploring broad composition spaces in 2D van der Waals magnets. The explicit linkage of a specific crystal structure to the emergence of ferromagnetism, backed by both experiment and DFT, would be useful for guiding targeted synthesis of higher-Tc materials. The combination of combinatorial libraries, rapid XRD clustering, and selective magnetic characterization is a methodological strength that could be adopted in other material families.

major comments (2)

[Results section describing ML clustering of XRD data] The unsupervised clustering of the 177 XRD patterns is load-bearing for the prerequisite claim, yet the manuscript provides no quantitative validation metrics (silhouette score, Davies-Bouldin index, or agreement with manually labeled reference patterns from known Fe-Ge-Te phases). Thin-film XRD artifacts (preferred orientation, strain broadening, finite-thickness effects) can produce overlapping or distorted patterns; without such checks it remains possible that the hexagonal cluster contains misclassified non-hexagonal material that still exhibits magnetism, undermining the strict correlation asserted in the abstract and discussion.
[Methods or supplementary information on DFT calculations] The DFT comparisons assume bulk, perfectly stoichiometric structures, but the manuscript does not report the specific computational settings (exchange-correlation functional, plane-wave cutoff, k-mesh density, or inclusion of van der Waals corrections). Because the experimental films are annealed sputtered layers whose local stoichiometry and defects may deviate from the modeled cells, the degree of agreement between calculated and measured magnetism cannot be evaluated rigorously.

minor comments (2)

[Abstract] The abstract states that ML clustering was used but does not name the algorithm (k-means, hierarchical, etc.) or the dimensionality-reduction step (PCA or raw patterns). Adding one sentence would improve reproducibility.
[Magnetic characterization subsection] Error bars or standard deviations on the SQUID and XMCD magnetization values are not mentioned; reporting them would strengthen the claim that magnetism is absent outside the hexagonal cluster.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed review. The comments highlight important aspects of validation and reproducibility that we have addressed in the revised manuscript. Below we respond point by point to the major comments.

read point-by-point responses

Referee: [Results section describing ML clustering of XRD data] The unsupervised clustering of the 177 XRD patterns is load-bearing for the prerequisite claim, yet the manuscript provides no quantitative validation metrics (silhouette score, Davies-Bouldin index, or agreement with manually labeled reference patterns from known Fe-Ge-Te phases). Thin-film XRD artifacts (preferred orientation, strain broadening, finite-thickness effects) can produce overlapping or distorted patterns; without such checks it remains possible that the hexagonal cluster contains misclassified non-hexagonal material that still exhibits magnetism, undermining the strict correlation asserted in the abstract and discussion.

Authors: We agree that quantitative validation metrics strengthen the presentation of the unsupervised clustering. In the revised manuscript we now include the silhouette score and Davies-Bouldin index for the selected number of clusters. We have also added a supplementary comparison of cluster centroids against reference XRD patterns of known Fe-Ge-Te phases from the literature. Regarding thin-film artifacts, the annealing conditions were chosen to promote crystallinity while minimizing strain; the resulting clusters show clear separation that aligns with the independently measured composition map and with the strict confinement of ferromagnetism to a single cluster. We have inserted a short discussion acknowledging possible artifacts and explaining why they do not alter the central phase-magnetism correlation. revision: yes
Referee: [Methods or supplementary information on DFT calculations] The DFT comparisons assume bulk, perfectly stoichiometric structures, but the manuscript does not report the specific computational settings (exchange-correlation functional, plane-wave cutoff, k-mesh density, or inclusion of van der Waals corrections). Because the experimental films are annealed sputtered layers whose local stoichiometry and defects may deviate from the modeled cells, the degree of agreement between calculated and measured magnetism cannot be evaluated rigorously.

Authors: We thank the referee for noting the missing computational details. The revised Methods section now explicitly states the use of the PBE functional, a plane-wave cutoff of 500 eV, a k-point mesh density of 0.03 Å⁻¹, and DFT-D3 van der Waals corrections. We have added text clarifying that the DFT calculations provide a qualitative reference for ideal bulk structures and that quantitative agreement with experiment is limited by possible defects and off-stoichiometry in the sputtered films. This limitation is now discussed in the context of the observed experimental correlation between the hexagonal phase and ferromagnetism. revision: yes

Circularity Check

0 steps flagged

No significant circularity in experimental derivation chain

full rationale

The paper presents an experimental workflow involving combinatorial thin-film synthesis, high-throughput WDS/XRD/resistance measurements, unsupervised clustering of the XRD dataset into structural groups, SQUID/XMCD magnetometry on those groups, and direct comparison to independent DFT models of magnetization and structure. The central claim that the hexagonal phase is a prerequisite for ferromagnetism follows from empirical correlation between the observed magnetic signals and the hexagonal XRD cluster, without any reduction of results to fitted parameters renamed as predictions, self-definitional loops, or load-bearing self-citations. The derivation remains self-contained against external experimental benchmarks and separate DFT calculations.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the validity of the high-throughput characterization and the interpretation of ML clusters as structural phases, plus the accuracy of DFT for magnetism prediction.

axioms (1)

domain assumption Unsupervised machine learning can reliably cluster XRD patterns into groups corresponding to distinct structural phases.
Central to identifying phase distribution without prior labeling.

pith-pipeline@v0.9.0 · 5827 in / 1388 out tokens · 55892 ms · 2026-05-20T09:47:34.272263+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Foundation/AbsoluteFloorClosure.lean reality_from_one_distinction unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

We leverage unsupervised machine learning to cluster the XRD dataset into groups of compositions with similar structural phases... Gaussian mixture model was used to estimate the probability distribution for N (N = 5) clusters.
IndisputableMonolith/Foundation/AlexanderDuality.lean alexander_duality_circle_linking unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

Our results demonstrate that the hexagonal crystal structure is a critical prerequisite for ferromagnetism in this system

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

12 extracted references · 12 canonical work pages · 1 internal anchor

[1]

Conclusions Our results demonstrate the successful growth of thin film Fe-Ge-Te via magnetron sputtering. We were able to synthesize several well-known ferromagnets within an expanded compositional space, despite the 12 fact that the entire wafer had to be processed together, inevitably leading to potentially suboptimal processing conditions for some comp...

work page doi:10.1038/nature22060 2017
[2]

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(9) Verchenko, V

https://doi.org/10.3390/molecules28217244. (9) Verchenko, V . Yu.; Tsirlin, A. A.; Sobolev, A. V .; Presniakov, I. A.; Shevelkov, A. V . Ferromagnetic Order, Strong Magnetocrystalline Anisotropy, and Magnetocaloric Effect in the Layered Telluride Fe3−δGeTe2. Inorg. Chem. 2015, 54 (17), 8598–8607. https://doi.org/10.1021/acs.inorgchem.5b01260. (10) Deng, Y...

work page doi:10.3390/molecules28217244 2015
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(20) Zhou, L.; Huang, J.; Tang, M.; Qiu, C.; Qin, F.; Zhang, C.; Li, Z.; Wu, D.; Yuan, H

https://doi.org/10.1038/s41467-023-37917-8. (20) Zhou, L.; Huang, J.; Tang, M.; Qiu, C.; Qin, F.; Zhang, C.; Li, Z.; Wu, D.; Yuan, H. Gate-Tunable Spin Valve Effect in Fe3GeTe2-Based van Der Waals Heterostructures. InfoMat 2023, 5 (3), e12371. https://doi.org/10.1002/inf2.12371. (21) Alghamdi, M.; Lohmann, M.; Li, J.; Jothi, P. R.; Shao, Q.; Aldosary, M.;...

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https://doi.org/10.1038/s42005-022-00921-3. (24) May, A. F.; Calder, S.; Cantoni, C.; Cao, H.; McGuire, M. A. Magnetic Structure and Phase Stability of the van Der Waals Bonded Ferromagnet ${\mathrm{Fe}}_{3\ensuremath{-}x}{\mathrm{GeTe}}_{2}$. Phys. Rev. B 2016, 93 (1), 014411. https://doi.org/10.1103/PhysRevB.93.014411. (25) Kim, D.; Lee, C.; Jang, B. G....

work page doi:10.1038/s42005-022-00921-3 2016
[7]

Magnetic Order in Pulsed Laser Deposited (Fe,Ni)5GeTe2 Films

https://doi.org/10.48550/arXiv.2508.13085. (35) Seo, J.; Kim, D. Y .; An, E. S.; Kim, K.; Kim, G.-Y .; Hwang, S.-Y .; Kim, D. W.; Jang, B. G.; Kim, H.; Eom, G.; Seo, S. Y .; Stania, R.; Muntwiler, M.; Lee, J.; Watanabe, K.; Taniguchi, T.; Jo, Y . J.; Lee, J.; Min, B. I.; Jo, M. H.; Yeom, H. W.; Choi, S.-Y .; Shim, J. H.; Kim, J. S. Nearly Room Temperature...

work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2508.13085 2020
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(37) Ng, A.; Jordan, M.; Weiss, Y

https://doi.org/10.48550/arXiv.2302.00553. (37) Ng, A.; Jordan, M.; Weiss, Y . On Spectral Clustering: Analysis and an Algorithm. In Advances in Neural Information Processing Systems; MIT Press, 2001; V ol

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(39) Carra, P.; Thole, B. T.; Altarelli, M.; Wang, X. X-Ray Circular Dichroism and Local Magnetic Fields. Phys. Rev. Lett. 1993, 70 (5), 694–697. https://doi.org/10.1103/PhysRevLett.70.694. (40) Yamagami, K.; Fujisawa, Y .; Pardo-Almanza, M.; Smith, B. R. M.; Sumida, K.; Takeda, Y .; Okada, Y . Enhanced $d\text{\ensuremath{-}}p$ Hybridization Intertwined ...

work page doi:10.1103/physrevlett.70.694 1993
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https://doi.org/10.1038/s41699-022-00285-w. (53) Kim, D.; Park, S.; Lee, J.; Yoon, J.; Joo, S.; Kim, T.; Min, K.; Park, S.-Y .; Kim, C.; Moon, K.-W.; Lee, C.; Hong, J.; Hwang, C. Antiferromagnetic Coupling of van Der Waals Ferromagnetic Fe3GeTe2. Nanotechnology 2019, 30 (24), 245701. https://doi.org/10.1088/1361-6528/ab0a37. (54) Alahmed, L.; Nepal, B.; M...

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The van Der Waals Ferromagnets Fe5–δGeTe2 and Fe5–δ–xNixGeTe2–Crystal Structure, Stacking Faults, and Magnetic Properties

(71) Stahl, J.; Shlaen, E.; Johrendt, D. The van Der Waals Ferromagnets Fe5–δGeTe2 and Fe5–δ–xNixGeTe2–Crystal Structure, Stacking Faults, and Magnetic Properties. Zeitschrift für anorganische und allgemeine Chemie 2018, 644 (24), 1923–1929. https://doi.org/10.1002/zaac.201800456. (72) Bouad, N.; Chapon, L.; Marin-Ayral, R.-M.; Bouree-Vigneron, F.; Tedena...

work page doi:10.1002/zaac.201800456 2018
[12]

https://doi.org/10.1038/s41524-019-0199-7

work page doi:10.1038/s41524-019-0199-7

[1] [1]

Conclusions Our results demonstrate the successful growth of thin film Fe-Ge-Te via magnetron sputtering. We were able to synthesize several well-known ferromagnets within an expanded compositional space, despite the 12 fact that the entire wafer had to be processed together, inevitably leading to potentially suboptimal processing conditions for some comp...

work page doi:10.1038/nature22060 2017

[2] [2]

Molecules 2023, 28 (21),

Ferromagnets. Molecules 2023, 28 (21),

work page 2023

[3] [3]

(9) Verchenko, V

https://doi.org/10.3390/molecules28217244. (9) Verchenko, V . Yu.; Tsirlin, A. A.; Sobolev, A. V .; Presniakov, I. A.; Shevelkov, A. V . Ferromagnetic Order, Strong Magnetocrystalline Anisotropy, and Magnetocaloric Effect in the Layered Telluride Fe3−δGeTe2. Inorg. Chem. 2015, 54 (17), 8598–8607. https://doi.org/10.1021/acs.inorgchem.5b01260. (10) Deng, Y...

work page doi:10.3390/molecules28217244 2015

[4] [4]

(20) Zhou, L.; Huang, J.; Tang, M.; Qiu, C.; Qin, F.; Zhang, C.; Li, Z.; Wu, D.; Yuan, H

https://doi.org/10.1038/s41467-023-37917-8. (20) Zhou, L.; Huang, J.; Tang, M.; Qiu, C.; Qin, F.; Zhang, C.; Li, Z.; Wu, D.; Yuan, H. Gate-Tunable Spin Valve Effect in Fe3GeTe2-Based van Der Waals Heterostructures. InfoMat 2023, 5 (3), e12371. https://doi.org/10.1002/inf2.12371. (21) Alghamdi, M.; Lohmann, M.; Li, J.; Jothi, P. R.; Shao, Q.; Aldosary, M.;...

work page doi:10.1038/s41467-023-37917-8 2023

[5] [5]

Commun Phys 2022, 5 (1),

Ultrathin Films. Commun Phys 2022, 5 (1),

work page 2022

[6] [6]

(24) May, A

https://doi.org/10.1038/s42005-022-00921-3. (24) May, A. F.; Calder, S.; Cantoni, C.; Cao, H.; McGuire, M. A. Magnetic Structure and Phase Stability of the van Der Waals Bonded Ferromagnet ${\mathrm{Fe}}_{3\ensuremath{-}x}{\mathrm{GeTe}}_{2}$. Phys. Rev. B 2016, 93 (1), 014411. https://doi.org/10.1103/PhysRevB.93.014411. (25) Kim, D.; Lee, C.; Jang, B. G....

work page doi:10.1038/s42005-022-00921-3 2016

[7] [7]

Magnetic Order in Pulsed Laser Deposited (Fe,Ni)5GeTe2 Films

https://doi.org/10.48550/arXiv.2508.13085. (35) Seo, J.; Kim, D. Y .; An, E. S.; Kim, K.; Kim, G.-Y .; Hwang, S.-Y .; Kim, D. W.; Jang, B. G.; Kim, H.; Eom, G.; Seo, S. Y .; Stania, R.; Muntwiler, M.; Lee, J.; Watanabe, K.; Taniguchi, T.; Jo, Y . J.; Lee, J.; Min, B. I.; Jo, M. H.; Yeom, H. W.; Choi, S.-Y .; Shim, J. H.; Kim, J. S. Nearly Room Temperature...

work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2508.13085 2020

[8] [8]

(37) Ng, A.; Jordan, M.; Weiss, Y

https://doi.org/10.48550/arXiv.2302.00553. (37) Ng, A.; Jordan, M.; Weiss, Y . On Spectral Clustering: Analysis and an Algorithm. In Advances in Neural Information Processing Systems; MIT Press, 2001; V ol

work page doi:10.48550/arxiv.2302.00553 2001

[9] [9]

doi:10.1103/PhysRevLett.70.694 , url =

(39) Carra, P.; Thole, B. T.; Altarelli, M.; Wang, X. X-Ray Circular Dichroism and Local Magnetic Fields. Phys. Rev. Lett. 1993, 70 (5), 694–697. https://doi.org/10.1103/PhysRevLett.70.694. (40) Yamagami, K.; Fujisawa, Y .; Pardo-Almanza, M.; Smith, B. R. M.; Sumida, K.; Takeda, Y .; Okada, Y . Enhanced $d\text{\ensuremath{-}}p$ Hybridization Intertwined ...

work page doi:10.1103/physrevlett.70.694 1993

[10] [10]

(53) Kim, D.; Park, S.; Lee, J.; Yoon, J.; Joo, S.; Kim, T.; Min, K.; Park, S.-Y .; Kim, C.; Moon, K.-W.; Lee, C.; Hong, J.; Hwang, C

https://doi.org/10.1038/s41699-022-00285-w. (53) Kim, D.; Park, S.; Lee, J.; Yoon, J.; Joo, S.; Kim, T.; Min, K.; Park, S.-Y .; Kim, C.; Moon, K.-W.; Lee, C.; Hong, J.; Hwang, C. Antiferromagnetic Coupling of van Der Waals Ferromagnetic Fe3GeTe2. Nanotechnology 2019, 30 (24), 245701. https://doi.org/10.1088/1361-6528/ab0a37. (54) Alahmed, L.; Nepal, B.; M...

work page doi:10.1038/s41699-022-00285-w 2019

[11] [11]

The van Der Waals Ferromagnets Fe5–δGeTe2 and Fe5–δ–xNixGeTe2–Crystal Structure, Stacking Faults, and Magnetic Properties

(71) Stahl, J.; Shlaen, E.; Johrendt, D. The van Der Waals Ferromagnets Fe5–δGeTe2 and Fe5–δ–xNixGeTe2–Crystal Structure, Stacking Faults, and Magnetic Properties. Zeitschrift für anorganische und allgemeine Chemie 2018, 644 (24), 1923–1929. https://doi.org/10.1002/zaac.201800456. (72) Bouad, N.; Chapon, L.; Marin-Ayral, R.-M.; Bouree-Vigneron, F.; Tedena...

work page doi:10.1002/zaac.201800456 2018

[12] [12]

https://doi.org/10.1038/s41524-019-0199-7

work page doi:10.1038/s41524-019-0199-7