Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.29235

Disorder-Mediated Magnetism in Transition-Metal Dichalcogenide Monolayers: XS$_2$ and XSe$_2$ (X = Ti, V, Co, Mo, and Ru)

Chi-Cheng Lee·Yukiko Yamada-Takamura·Taisuke Ozaki
View original source ↗Read source document ↗Export metadata ↓

Abstract

Magnetism in transition-metal dichalcogenide monolayers has attracted considerable attention, yet its nature remains elusive, particularly in monolayer 1T-VSe$_2$. Our first-principles calculations show that Stoner instability can drive ferromagnetism in XS$_2$ and XSe$_2$ monolayers (X = Ti, V, Co, Mo, and Ru), associated with a reduced density of states at the Fermi level. We demonstrate that mixing nonmagnetic Ti, Co, Mo, and Ru disulfides or diselenides can induce magnetism. Conversely, mixing magnetic VS$_2$ or VSe$_2$ with other compounds can completely suppress magnetism. Both scenarios can lower the total energy. Moreover, substituting a small number of V atoms with Co defects can also disrupt ferromagnetism in 1T-V(S,Se)$_2$. Even in pristine 1T-VSe$_2$, we find ferrimagnetic configurations with reversed local V moments that are nearly degenerate with the ferromagnetic state. Their competition may contribute to the suppression of long-range ferromagnetic order. This reduced energy penalty for magnetic and structural disorder arises from a suppressed density of states at the Fermi level, resembling the mechanism underlying the Peierls transition. Electronically driven energy lowering near the Fermi level may therefore be widespread among transition-metal dichalcogenide monolayers in various forms.

Explore related subjects

cond-mat.mtrl-sci

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Chi-Cheng Lee, Yukiko Yamada-Takamura, Taisuke Ozaki. 2026-10-21. Disorder-Mediated Magnetism in Transition-Metal Dichalcogenide Monolayers: XS$_2$ and XSe$_2$ (X = Ti, V, Co, Mo, and Ru). https://arxiv.org/abs/2610.29235

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Mechanical Analyses using Discrete Harmonic Expansions of Grain-Based Field Data

A methodology for computing expansion basis functions using discrete harmonic modes is presented. The discrete harmonic modes are determined grain-by-grain for virtual polycrystals for which finite element meshes are available. The expansion weights associated with representing field variables over grain domains are determined by exploiting the orthogonality of the harmonic modes. The methodology is demonstrated with the representation of the axial stress distributions during tensile loading of a polycrystalline sample. An open source code, MechMonics, is available to researchers wishing to use the methodology to analyze data.

cond-mat.mtrl-sci↗
arXiv2026

Precipitation strengthening as a collective multi-dislocation phenomenon

Precipitation strengthening is a cornerstone of physical metallurgy, delivering otherwise unattainable combinations of strength and ductility. The approach relies on nanoscale precipitates that impede the motion of dislocations, the primary carriers of metal plasticity. Historically, precipitation strengthening has been rationalized via two idealized, limiting mechanisms: dislocations either cut through or bow around precipitates. However, under many technologically relevant conditions, multiple dislocations can interact with a precipitate at the same time, potentially giving rise to distinct precipitate-dislocation dynamics that in situ experiments are yet to fully resolve. Here, using large-scale atomistic simulations (100 million atoms) that capture these dynamics, we demonstrate that in this regime, the cutting-versus-bowing dichotomy is incomplete: strengthening arises as an emergent phenomenon driven by concurrent multi-dislocation interactions. These interactions simultaneously induce dislocation accumulation at interfaces, storage within precipitates, and precipitate-mediated multiplication inside the matrix. Our findings establish a distinct mechanistic regime of precipitation strengthening beyond traditional models and provide a quantitative foundation for predicting its behavior.

cond-mat.mtrl-sci↗
arXiv2026

A Combined Tight Binding with Machine Learning Potential Model for Magnesium Compounds

We present a model for magnesium-based systems that combines density functional tight binding (DFTB) with MACE, a machine learning interatomic potential (DFTB+MACE). In this model, the conventional repulsive potential, pair potential, is replaced by a many-body MACE potential. The MACE component of the model is trained on the difference between density functional theory (DFT) energies and forces and the corresponding DFTB values, but neglecting the pair potential contribution. Using this model we performed structural relaxation of MgO-CO2 adsorption systems, molecular dynamics calculations of water clusters and phonon spectrum calculations of stable fcc-MgO and metastable bcc-MgO structures. We compare the performance of our model with a pure MACE model and with DFT. We demonstrate that the DFTB+MACE model achieves improved accuracy relative to DFTB with a pair potential, in many cases with only a moderate increase in computational cost. In addition, it can provide electronic structures that most of the machine learning potentials cannot. The training dataset, originally developed for MACE, may not fully represent all regions of the potential surface we may encounter during simulations. Expanding the dataset for a wider potential surface is expected to further enhance predictive accuracy of DFTB+MACE model. Overall, the resulting DFTB+MACE framework enables simulations at length and time scales beyond the reach of first-principles methods while retaining an explicit description of electronic structures, making it particularly attractive for studying charge-transfer in materials.

cond-mat.mtrl-sci↗