arXiv subjects

Mohammad Bagheri

Publications and source records attributed to Mohammad Bagheri.

10 recordsLinked to original sources

arXiv2026

Toward the Ultimate Limit: Elemental Metals in One Dimension

Low-dimensional materials exhibit extraordinary properties that make them promising candidates for advanced technologies. Although they have been investigated extensively, most of the research has focused on layered two-dimensional (2D) materials. Here, inspired by recent advances in atomically thin metallenes, we further reduce dimensionality and use density-functional theory simulations to study the geometry, energetics, elasticity, and electronic structure of 40 non-magnetic one-dimensional (1D) atomic chains of elemental metals. We find that nearly all chains have a buckled ground state, nine chains are distorted, and three chains---Cd, Hg, and Sr---are semiconducting with an electronic gap. We also find that transition metals retain a substantial fraction of their 3D bulk cohesive energy even in 1D chains. We assessed chains' dynamical stabilities by molecular dynamics simulations and found that 26 of them are thermodynamically stable at 100 K. Finally, we performed chain pulling simulations to investigate the straightening dynamics of selected stable chains. Given that experimental techniques have recently reached the 1D-chain limit, our systematic study provides a foundation and timely guide to accelerating synthesis and characterization of these materials.

cond-mat.mtrl-sci↗

arXiv2026

Machine-Learning-Accelerated Metallene Stabilization from High-Throughput Sandwich Modeling

Metallenes have appealing properties, but stabilizing them in a monolayer phase poses challenges for their synthesis. A recent experiment showed that the van der Waals squeezing method can stabilize certain metallenes in a MoS2 sandwich. This pioneering work motivates systematic studies, but such studies are experimentally impractical, while first-principles modeling remains prohibitive. Here, armed with universal machine-learning interatomic potentials, we constructed 1620 metallene sandwich heterostructures containing 6 different sandwich layers and 45 metals. We performed phonon calculations, which revealed 1208 dynamically stable structures. We found that transition-metal dichalcogenides, particularly MoSe2, are highly effective in stabilizing metallenes. Specifically, buckled hexagonal and honeycomb crystal lattices exhibit the greatest stability. We further evaluated the thermal stability of selected heterostructures with density-functional theory molecular dynamics simulations at room temperature. By uncovering the physical and chemical factors governing the stabilization of metallenes, our results provide systematic insights to guide and accelerate synthesis for future applications.

cond-mat.mtrl-sci↗

arXiv2026

Lattice and Orbital-Resolved Fermiology of Metallenes

Atomically thin metallenes have emerged as a new member of the two-dimensional (2D) materials family. Recent experimental realization of metallenes in the {\AA}ngstr\"om limit has further intensified interest in this class of 2D materials. However, achieving sub-atomic insight into them demands the most detailed and systematic characterization of their electronic structure. Such understanding is essential for the rational design and exploitation of their properties in plasmonics, catalysis, and quantum optics. Existing electronic-structure studies are either scattered or focus on a few selected systems, and a comprehensive view of their band structures and Fermi surfaces remains missing. Here, we address this gap by studying 45 elemental metallenes in six monolayer lattices (honeycomb, square, hexagonal, and their buckled forms) using density-functional theory. We found that lattice type primarily fixes the shape and radial placement of the Fermi-lines, while out-of-plane buckling introduces controlled modifications: it shortens long straight Fermi-line segments, and occasionally creates, removes, or merges small Fermi-line pockets. The electronic configuration determines which orbital type dominates the Fermi level. We summarized Fermiology using a single score for each element, termed pocketness, derived from four descriptors that combine element properties (symmetry, coordination) with electronic characteristics (dispersion, Fermi-surface topology). This score enables targeted angle-resolved photoemission spectroscopy (ARPES) tests, controlled Lifshitz transitions, and provides a predictive basis for transport and device applications.

cond-mat.mtrl-sci↗

arXiv2026

Lateral Graphene-Metallene Interfaces at the Nanoscale

Metallenes are atomically thin, nonlayered two-dimensional materials. While they have appealing properties, their isotropic metallic bonding makes their stabilization difficult and presents considerable challenges to their synthesis and practical applications. However, their stabilization can still be achieved by suspending them in the pores of two-dimensional template materials, making the properties of lateral interfaces of metallenes scientifically relevant. Here, we combined density-functional theory and universal machine-learning interatomic potentials to study lateral interfaces between graphene and 45 metallenes with various profiles. We optimized the interfaces and analyzed their energies, electronic structures, and stabilities at room temperature, defect formations, and structural deformations. While broad trends were identified using machine-learning analysis of all interfaces, density-functional theory was the main tool for studying the microscopic properties of selected elements. We found that the interfaces are the most stable energetically and with respect to lattice mismatch, defect formation, and lateral strain when their profiles were geometrically smooth. The most stable interfaces are found for transition metals. In addition, we demonstrate how universal machine-learning interatomic potentials now offer the accuracy required for the modeling of graphene-metallene interfaces. By systematically expanding the understanding of metallenes' interface properties, we hope these results guide and accelerate their synthesis to enable future applications and benefit from metallenes' appealing properties.

cond-mat.mtrl-sci↗

arXiv2025

Massive Discovery of Low-Dimensional Materials from Universal Computational Strategy

Low-dimensional materials have attractive properties that drive intense efforts for novel materials discovery. However, experiments are tedious for systematic discovery, and present computational methods are often tuned to two-dimensional (2D) materials, overlooking other low-dimensional materials. Here, we combined universal machine-learning interatomic potentials (UMLIPs) and an advanced, interatomic force constant (FC) -based dimensionality classification method to make a massive discovery of novel low-dimensional materials. We first benchmarked UMLIPs' first-principles-level accuracy in quantifying FCs and calculated phonons for 35,689 materials from the Materials Project database. We then used the FC-based method for dimensionality classification to discover 9139 low-dimensional materials, including 1838 0D clusters, 1760 1D chains, 3057 2D sheets/layers, and 2484 mixed-dimensionality materials, all of which conventional geometric descriptors have not recognized. By calculating the binding energies for the discovered 2D materials, we also identified 887 sheets that could be easily or potentially exfoliated from their parent bulk structures.

cond-mat.mtrl-sci↗

arXiv2025

Screening of material defects using universal machine-learning interatomic potentials

Finding new materials with previously unknown atomic structure or materials with optimal set of properties for a specific application greatly benefits from computational modeling. Recently, such screening has been dramatically accelerated by the invent of universal machine-learning interatomic potentials that offer first principles accuracy at orders of magnitude lower computational cost. Their application to the screening of defects with desired properties or to finding new stable compounds with high density of defects, however, has not been explored. Here, we show that the universal machine-learning interatomic potentials have reached sufficient accuracy to enable large-scale screening of defective materials. We carried out vacancy calculations for 86 259 materials in the Materials Project database and analyzed the formation energies in terms of oxidation numbers. We further demonstrate the application of these models for finding new materials at or below the convex hull of known materials and for simulated etching of low-dimensional materials.

cond-mat.mtrl-sci↗

arXiv2025

Atomically Thin Metallenes at the Edge

Atomically thin metallenes are a new family of materials representing the ultimate limit of a thin free-electron gas for novel applications. Although metallene research has gained traction, limited attention has been paid to the properties of their ubiquitous edges. Here, we use density-functional theory simulations to investigate various edges of Mg, Cu, Y, Au, and Pb metallenes with hexagonal and buckled honeycomb lattices. Investigating relaxations, energies, stresses, and electronic structures at the edge, we find that some properties have clear trends while others are sensitive to both element and lattice type. Given that edge properties are fundamental to metallene stability and interactions in lateral heterostructures, their detailed understanding will help guide the development of metallene synthesis and applications.

cond-mat.mtrl-sci↗

arXiv2022

High-throughput computation of Raman spectra from first principles

Raman spectroscopy is a widely-used non-destructive material characterization method, which provides information about the vibrational modes of the material and therefore of its atomic structure and chemical composition. Interpretation of the spectra requires comparison to known references and to this end, experimental databases of spectra have been collected. Reference Raman spectra could also be simulated using atomistic first-principles methods but these are computationally demanding and thus the existing databases of computational Raman spectra are fairly small. In this work, we developed an optimized workflow to calculate the Raman spectra more efficiently compared to existing approaches. The workflow was benchmarked and validated by comparison to experiments and previous computational methods for select technologically relevant material systems. Using the workflow, we performed high-throughput calculations for a large set of materials (5099) belonging to many different material classes, and collected the results to a database. Finally, the contents of database are analyzed and the calculated spectra are shown to agree well with the experimental ones.

physics.comp-ph↗

arXiv2022

Screening 0D materials for 2D nanoelectronics applications

As nanoelectronic devices based on two-dimensional (2D) materials are moving towards maturity, optimization of the properties of the active 2D material must be accompanied by equal attention to optimizing the properties of and the interfaces to the other materials around it, such as electrodes, gate dielectrics, and the substrate. While these are usually either 2D or 3D materials, recently K. Liu et al. [Nat. Electron. 4, 906 (2021)] reported on the use of zero-dimensional (0D) material, consisting of vdW-bonded Sb$_2$O$_3$ clusters, as a highly promising insulating substrate and gate dielectric. Here, we report on computational screening study to find promising 0D materials for use in nanoelectronics applications, in conjunction with 2D materials in particular. By combining a database and literature searches, we found 16 materials belonging to 6 structural prototypes with high melting points and high band gaps, and a range of static dielectric constants. We carried out additional first-principles calculations to evaluate selected technologically relevant material properties, and confirmed that all these materials are van der Waals-bonded, thus allowing for facile separation of 0D clusters from the 3D host and also weakly perturbing the electronic properties of the 2D material after deposition.

cond-mat.mtrl-sci↗

arXiv2021

Fermiology of two-dimensional titanium carbide and nitride MXenes

MXenes are a family two-dimensional transition metal carbide and nitride materials, which often exhibit very good metallic conductivity and are thus of great interest for applications in, e.g., flexible electronics, electrocatalysis, and electromagnetic interference shielding. However, surprisingly little is known about the fermiology of MXenes, i.e, the shape and size of their Fermi-surfaces, and its effect on the material properties. One reason for this may be that MXene surfaces are almost always covered by a mixture of functional groups, and studying Fermi-surfaces of disordered systems is cumbersome. Here, we study fermiology of four common Ti-based MXenes as a function of the surface functional group composition. We first calculate the effective band structures of systems with explicit mixed surfaces and observe gradual evolution in the filling of the Ti-d band and resulting shift of Fermi-level. We then demonstrate that these band structures can be closely approximated by using pseudohydrogenated surfaces, and also compare favorably to the experimental angle-resolved photoemission spectroscopy results. By modifying the pseudohydrogen charge we then proceed to plot Fermi-surfaces for all systems and extract their properties, such as the Fermi-surface area and average Fermi-velocity. These are in turn used to evaluate the electrical conductivity with the relaxation time fitted to experimentally measured conductivities.

cond-mat.mtrl-sci↗