arXiv subjects

Qimin Yan

Publications and source records attributed to Qimin Yan.

At least 37 records · Page 2Linked to original sources

arXiv2023

Exciton Confinement in Two-Dimensional, In-Plane, Quantum Heterostructures

Two-dimensional (2D) semiconductors are promising candidates for optoelectronic application and quantum information processes due to their inherent out-of-plane 2D confinement. In addition, they offer the possibility of achieving low-dimensional in-plane exciton confinement, similar to zero-dimensional quantum dots, with intriguing optical and electronic properties via strain or composition engineering. However, realizing such laterally confined 2D monolayers and systematically controlling size-dependent optical properties remain significant challenges. Here, we report the observation of lateral confinement of excitons in epitaxially grown in-plane MoSe2 quantum dots (~15-60 nm wide) inside a continuous matrix of WSe2 monolayer film via a sequential epitaxial growth process. Various optical spectroscopy techniques reveal the size-dependent exciton confinement in the MoSe2 monolayer quantum dots with exciton blue shift (12-40 meV) at a low temperature as compared to continuous monolayer MoSe2. Finally, single-photon emission was also observed from the smallest dots at 1.6 K. Our study opens the door to compositionally engineered, tunable, in-plane quantum light sources in 2D semiconductors.

cond-mat.mes-hall↗

arXiv2022

Controllable optical and magneto-optical properties of magnetic CrI3 nanoribbons

A monolayer of CrI3 has an amazing ferromagnetic ground-state. In this work, we calculate band structures and magnetic moments of tensile-strained and bent zigzag CrI3 nanoribbons with density functional theory. The edge iodine atoms form flat low-lying conduction bands and couple with chromium atoms ferromagnetically, while the non-edge iodine atoms weakly couple antiferromagnetically. CrI3 nanoribbons have a nearly equal preference for the out-of-plane and in-plane magnetic moment configurations, slightly favoring the in-plane one. We also calculate optical absorption with many-body perturbation GW-BSE (Bethe-Salpeter equation) and investigate magneto-optical properties, including magnetic dichroism, Faraday and magneto-optical Kerr effects. The low-energy dark excitons are mainly from transitions between electrons and holes with unlike spins and are non-Frenkel-like, while the bright excitons have mixed spin configurations. Tensile strains and bending manifestly modulate the absorption spectra and magneto-optical properties of CrI3 nanoribbons within a technologically important photon energy-range of ~1.0-2.0 eV, suggesting a potential application in tunable magnetic optoelectronics.

cond-mat.mtrl-sci↗

arXiv2022

Incorporation of density scaling constraint in density functional design via contrastive representation learning

In a data-driven paradigm, machine learning (ML) is the central component for developing accurate and universal exchange-correlation (XC) functionals in density functional theory (DFT). It is well known that XC functionals must satisfy several exact conditions and physical constraints, such as density scaling, spin scaling, and derivative discontinuity. In this work, we demonstrate that contrastive learning is a computationally efficient and flexible method to incorporate a physical constraint in ML-based density functional design. We propose a schematic approach to incorporate the uniform density scaling property of electron density for exchange energies by adopting contrastive representation learning during the pretraining task. The pretrained hidden representation is transferred to the downstream task to predict the exchange energies calculated by DFT. The electron density encoder transferred from the pretraining task based on contrastive learning predicts exchange energies that satisfy the scaling property, while the model trained without using contrastive learning gives poor predictions for the scaling-transformed electron density systems. Furthermore, the model with pretrained encoder gives a satisfactory performance with only small fractions of the whole augmented dataset labeled, comparable to the model trained from scratch using the whole dataset. The results demonstrate that incorporating exact constraints through contrastive learning can enhance the understanding of density-energy mapping using neural network (NN) models with less data labeling, which will be beneficial to generalizing the application of NN-based XC functionals in a wide range of scenarios that are not always available experimentally but theoretically justified. This work represents a viable pathway toward the machine learning design of a universal density functional via representation learning.

physics.chem-ph↗

arXiv2022

2022 Roadmap for Materials for Quantum Technologies

Quantum technologies are poised to move the foundational principles of quantum physics to the forefront of applications. This roadmap identifies some of the key challenges and provides insights on materials innovations underlying a range of exciting quantum technology frontiers. Over the past decades, hardware platforms enabling different quantum technologies have reached varying levels of maturity. This has allowed for first proof-of-principle demonstrations of quantum supremacy, for example quantum computers surpassing their classical counterparts, quantum communication with reliable security guaranteed by laws of quantum mechanics, and quantum sensors uniting the advantages of high sensitivity, high spatial resolution, and small footprints. In all cases, however, advancing these technologies to the next level of applications in relevant environments requires further development and innovations in the underlying materials. From a wealth of hardware platforms, we select representative and promising material systems in currently investigated quantum technologies. These include both the inherent quantum bit systems as well as materials playing supportive or enabling roles, and cover trapped ions, neutral atom arrays, rare earth ion systems, donors in silicon, color centers and defects in wide-band gap materials, two-dimensional materials and superconducting materials for single-photon detectors. Advancing these materials frontiers will require innovations from a diverse community of scientific expertise, and hence this roadmap will be of interest to a broad spectrum of disciplines.

quant-ph↗

arXiv2021

Antisite defect qubits in monolayer transition metal dichalcogenides

Being atomically thin and amenable to external controls, two-dimensional (2D) materials offer a new paradigm for the realization of patterned qubit fabrication and operation at room temperature for quantum information sciences applications. Here we show that the antisite defect in 2D transition metal dichalcogenides (TMDs) can provide a controllable solid-state spin qubit system. Using high-throughput atomistic simulations, we identify several neutral antisite defects in TMDs that lie deep in the bulk band gap and host a paramagnetic triplet ground state. Our in-depth analysis reveals the presence of optical transitions and triplet-singlet intersystem crossing processes for fingerprinting these defect qubits. As an illustrative example, we discuss the initialization and readout principles of an antisite qubit in WS2, which is expected to be stable against interlayer interactions in a multilayer structure for qubit isolation and protection in future qubit-based devices. Our study opens a new pathway for creating scalable, room-temperature spin qubits in 2D TMDs.

cond-mat.mes-hall↗

arXiv2020

Structure motif centric learning framework for inorganic crystalline systems

Incorporation of physical principles in a network-based machine learning (ML) architecture is a fundamental step toward the continued development of artificial intelligence for materials science and condensed matter physics. In this work, as inspired by the Pauling rule, we propose that structure motifs (polyhedral formed by cations and surrounding anions) in inorganic crystals can serve as a central input to a machine learning framework for crystalline inorganic materials. Taking metal oxides as examples, we demonstrated that, an unsupervised learning algorithm Motif2Vec is able to convert the presence of structure motifs and their connections in a large set of crystalline compounds into unique vector representations. The connections among complex materials can be largely determined by the presence of different structure motifs and their clustering information are identified by our Motif2Vec algorithm. To demonstrate the novel use of structure motif information, we show that a motif-centric learning framework can be effectively created by combining motif information with the recently developed atom-based graph neural networks to form an atom-motif dual graph network (AMDNet). Taking advantage of node and edge information on both atomic and motif level, the AMDNet is more accurate than an atom graph network in predicting electronic structure related material properties of metal oxides such as band gaps. The work illustrates the route toward fundamental design of graph neural network learning architecture for complex material properties by incorporating beyond-atom physical principles.

cond-mat.mtrl-sci↗

arXiv2020

Monolayer 2D semiconducting tellurides for high-mobility electronics

Discovery and design of two-dimensional (2D) materials with suitable band gaps and high carrier mobility is of vital importance for photonics, optoelectronics, and high-speed electronics. In this work, based on first principles calculations using density functional theory (DFT) with PBE and HSE functionals, we introduce a family of monolayer isostructural semiconducting tellurides M2N2Te8, with M = {Ti, Zr, Hf} and N= {Si, Ge}. These compounds have been identified to possess direct band gaps from 1.0 eV to 1.31 eV, which are well suited for photonics and optoelectronics applications. Additionally, anisotropic in-plane transport behavior is observed and small electron and hole (0.11 - 0.15 me) effective masses are identified along the dominant transport direction. Ultra-high carrier mobility is predicted for this family of 2D compounds which host great promise for potential applications in high-speed electronic devices. Detailed analysis of electronic structures reveals the origins of the promising properties of this unique class of 2D telluride materials.

cond-mat.mtrl-sci↗

arXiv2020

Graph Neural Network for Hamiltonian-Based Material Property Prediction

Development of next-generation electronic devices for applications call for the discovery of quantum materials hosting novel electronic, magnetic, and topological properties. Traditional electronic structure methods require expensive computation time and memory consumption, thus a fast and accurate prediction model is desired with increasing importance. Representing the interactions among atomic orbitals in any material, a material Hamiltonian provides all the essential elements that control the structure-property correlations in inorganic compounds. Effective learning of material Hamiltonian by developing machine learning methodologies therefore offers a transformative approach to accelerate the discovery and design of quantum materials. With this motivation, we present and compare several different graph convolution networks that are able to predict the band gap for inorganic materials. The models are developed to incorporate two different features: the information of each orbital itself and the interaction between each other. The information of each orbital includes the name, relative coordinates with respect to the center of super cell and the atom number, while the interaction between orbitals are represented by the Hamiltonian matrix. The results show that our model can get a promising prediction accuracy with cross-validation.

physics.comp-ph↗

arXiv2020

Two-dimensional MX family of Dirac materials with tunable electronic and topological properties

We propose a novel class of two-dimensional (2D) Dirac materials in the MX family (M=Be, Mg, Zn and Cd, X = Cl, Br and I), which exhibit graphene-like band structures with linearly-dispersing Dirac-cone states over large energy scales (0.8~1.8 eV) and ultra-high Fermi velocities comparable to graphene. The electronic and topological properties are found to be highly tunable and amenable to effective modulation via anion-layer substitution and vertical electric field. The electronic structures of several members of the family are shown to host a Van-Hove singularity (VHS) close to the energy of the Dirac node. The enhanced density-of-states associated with these VHSs could provide a mechanism for inducing topological superconductivity. The presence of sizable band gaps, ultra-high carrier mobilities, and small effective masses makes the MX family promising for electronics and spintronics applications.

cond-mat.mtrl-sci↗

arXiv2020

Quantum anomalous Hall effect in two-dimensional magnetic insulator heterojunctions

Recent years have witnessed tremendous success in the discovery of topological states of matter. Particularly, sophisticated theoretical methods in time-reversal-invariant topological phases have been developed, leading to the comprehensive search of crystal database and the prediction of thousands of new topological materials. In contrast, the discovery of magnetic topological phases that break time reversal is still limited to several exemplary materials because the coexistence of magnetism and topological electronic band structure is rare in a single compound. To overcome this challenge, we propose an alternative approach to realize the quantum anomalous Hall (QAH) effect, a typical example of magnetic topological phase, via engineering two-dimensional (2D) magnetic van der Waals heterojunctions. Instead of a single magnetic topological material, we search for the combinations of two 2D (typically trivial) magnetic insulator compounds with specific band alignment so that they can together form a type-III heterojunction with topologically non-trivial band structure. By combining the data-driven materials search, first principles calculations, and the symmetry-based analytical models, we identify 8 type-III heterojunctions consisting of 2D ferromagnetic insulator materials from a family of 2D monolayer MXY compounds (M = metal atoms, X = S, Se, Te, Y = F, Cl, Br, I) as a set of candidates for the QAH effect. In particular, we directly calculate the topological invariant (Chern number) and chiral edge states in the MnNF/MnNCl heterojunction with ferromagnetic stacking. This work illustrates how data-driven material science can be combined with symmetry-based physical principles to guide the search for novel heterojunction-based quantum materials hosting the QAH effect and other exotic quantum states in general.

cond-mat.mtrl-sci↗

arXiv2019

First-principles study of mechanical and electronic properties of bent monolayer transition metal dichalcogenides

The mechanical and electronic properties of transition metal dichalcogenide (TMD) monolayers corresponding to transition groups IV, VI, and X are explored under mechanical bending from first principles calculations using the strongly constrained and appropriately normed (SCAN) meta-GGA (MGGA). SCAN provides an accurate description of the phase stability of the TMD monolayers. Our calculated lattice parameters and other structural parameters agree well with experiment. We find that bending stiffness (or flexural rigidity) increases as the transition metal group goes from IV to X to VI, with the exception of PdTe$_2$. Variation in mechanical properties (local strain, physical thickness) and electronic properties (local charge density, band structure) with bending curvature is discussed. The local strain profile of these TMD monolayers under mechanical bending is highly non-uniform. The mechanical bending tunes not only the thickness of the TMD monolayers but also the local charge distribution as well as the band structures, adding more functionalization options to these materials.

cond-mat.mtrl-sci↗

arXiv2018

The key role of antibonding electron transfer in surface chemisorption and heterogeneous catalysis

The description of the chemical bond between a solid surface and an atom or a molecule is the fundamental basis for understanding a broad range of scientific problems in heterogeneous catalysis, semiconductor device fabrication, and fuel cells. Widespread understandings are based on the molecular orbital theory and focused on the degree of filling of antibonding surface-adsorbate states that weaken bonding on surfaces. The unoccupied antibonding surface-adsorbate states are often tacitly assumed to be irrelevant. Here, we show that most antibonding states become unoccupied because the electrons that would occupy these antibonding states are transferred to the lower-energy Fermi level. Such antibonding electron transfer goes beyond molecular orbital theory. It leads to an energy gain that largely controls the trends of surface adsorption strength and can serve as a primary descriptor for bonding on surfaces. This finding is illustrated from the first-principles study of hydrogen adsorption on MoS$_2$ surfaces. A clear linear relationship between the energies of antibonding electron transfer and hydrogen adsorption is identified. The hydrogen evolution reaction on MoS$_2$ is found to originate from the in-gap states induced by sulfur vacancies or edges. The effects of surface inhomogeneity (e.g., defects, step edges) on surface catalysis can be understood from the corresponding different energies gained from antibonding electron transfer. The emerging picture also offers a physically different explanation for the well-known $d$-band theory for hydrogen adsorption on transition metal surfaces.

cond-mat.mtrl-sci↗

arXiv2018

Atom2Vec: learning atoms for materials discovery

Exciting advances have been made in artificial intelligence (AI) during the past decades. Among them, applications of machine learning (ML) and deep learning techniques brought human-competitive performances in various tasks of fields, including image recognition, speech recognition and natural language understanding. Even in Go, the ancient game of profound complexity, the AI player already beat human world champions convincingly with and without learning from human. In this work, we show that our unsupervised machines (Atom2Vec) can learn the basic properties of atoms by themselves from the extensive database of known compounds and materials. These learned properties are represented in terms of high dimensional vectors, and clustering of atoms in vector space classifies them into meaningful groups in consistent with human knowledge. We use the atom vectors as basic input units for neural networks and other ML models designed and trained to predict materials properties, which demonstrate significant accuracy.

physics.comp-ph↗

arXiv2017

Prediction of $\mathrm{TiRhAs}$ as a Dirac Nodal Line Semimetal via First-Principles Calculations

Using first-principles calculations we predict that $\mathrm{TiRhAs}$, a previously synthesized compound, is a Dirac nodal line (DNL) semimetal. The DNL in this compound is found to be protected both by the combination of inversion and time-reversal symmetry, and by a reflection symmetry, in the absence of spin-orbit coupling (SOC). Our calculations show that band velocities associated with the nodal line have a high degree of directional anisotropy, with in-plane velocities $v_\perp$ perpendicular to the nodal line between $1.2-2.8\times10^5$ m/s. The crossings along the DNL are further found to exhibit a prominent and position-dependent tilt along directions perpendicular to the nodal line. We calculate $\mathbb{Z}_2$ indices based on parity eigenvalues at time-reversal invariant momenta and show that $\mathrm{TiRhAs}$ is topological. A tight-binding model fit from our first-principles calculations demonstrates the existence of two-dimensional drumhead surface states on the surface Brillouin zone. Based on the small gapping of the DNL upon inclusion of SOC and the clean Fermi surface free from trivial bands, $\mathrm{TiRhAs}$ is a promising candidate for further studies of the properties of topological semimetals.

cond-mat.mes-hall↗

arXiv2017

Impact of carrier localization on recombination in InGaN quantum wells and the efficiency of nitride light-emitting diodes: insights from theory and numerical simulations

We examine the effect of carrier localization due to random alloy fluctuations on the radiative and Auger recombination rates in InGaN quantum wells as a function of alloy composition, crystal orientation, carrier density, and temperature. Our results show that alloy fluctuations reduce individual transition matrix elements by the separate localization of electrons and holes, but this effect is overcompensated by the additional transitions enabled by translational symmetry breaking and the resulting lack of momentum conservation. Hence, we find that localization increases both radiative and Auger recombination rates, but that Auger recombination rates increase by one order of magnitude more than radiative rates. Furthermore, we demonstrate that localization has an overall detrimental effect on the efficiency-droop and green-gap problems of InGaN LEDs.

cond-mat.mtrl-sci↗

arXiv2017

Prediction of Ideal Topological Semimetals with Triply Degenerate Points in NaCu$_3$Te$_2$ Family

Triply degenerate points (TDPs) in band structure of a crystal can generate novel TDP fermions without high-energy counterparts. Although identifying ideal TDP semimetals, which host clean TDP fermions around the Fermi level ($E_F$) without coexisting of other quasiparticles, is critical to explore the intrinsic properties of this new fermion, it is still a big challenge and has not been achieved up to now. Here, we disclose an effective approach to search for ideal TDP semimetals via selective band crossing between antibonding $s$ and bonding $p$ orbitals along a line in the momentum space with $C_{3v}$ symmetry. Applying this approach, we have successfully identified the NaCu$_3$Te$_2$ family of compounds to be ideal TDP semimetals, where two and only two pairs of TDPs are located around the $E_F$. Moreover, we reveal an interesting mechanism to modulate energy splitting between a pair of TDPs, and illustrate the intrinsic features of TDP Fermi arcs in these ideal TDP semimetals.

cond-mat.mtrl-sci↗

arXiv2017

Negative Poisson's Ratio in 1T-Type Crystalline Two-Dimensional Transition Metal Dichalcogenides

Materials with a negative Poisson's ratio, also known as auxetic materials, exhibit unusual and counterintuitive mechanical behavior - becoming fatter in cross-section when stretched. Such behavior is mostly attributed to some special reentrant or hinged geometric structures regardless the chemical composition and electronic structure of a material. Here, using first principles calculations, we report a new class of auxetic single-layer two-dimensional (2D) materials, i.e., the 1T-type monolayer crystals of groups 6-7 transition-metal dichalcogenides, MX$_2$ (M = Mo, W, Tc, Re; X = S, Se, Te). These materials have a crystal structure distinct from all other known auxetic materials. They exhibit an intrinsic in-plane negative Poisson's ratio, which is dominated by the electronic effects. We attribute the occurrence of such auxetic behavior to the strong coupling between the chalcogen p orbitals and the intermetal t$_{2g}$-bonding orbitals within the basic triangular pyramid structure unit. The unusual auxetic behavior in combination with other remarkable properties of monolayer 2D materials could lead to novel multi-functionalities.

cond-mat.mtrl-sci↗

arXiv2014

First-principles theory of nonradiative carrier capture via multiphonon emission

We develop a practical first-principles methodology to determine nonradiative carrier capture coefficients at defects in semiconductors. We consider transitions that occur via multiphonon emission. Parameters in the theory, including electron-phonon coupling matrix elements, are computed consistently using state-of-the-art electronic structure techniques based on hybrid density functional theory. These provide a significantly improved description of bulk band structures, as well as defect geometries and wavefunctions. In order to properly describe carrier capture processes at charged centers, we put forward an approach to treat the effect of long-range Coulomb interactions on scattering states in the framework of supercell calculations. We also discuss the choice of initial conditions for a perturbative treatment of carrier capture. As a benchmark, we apply our theory to several hole-capturing centers in GaN and ZnO, materials of high technological importance in which the role of defects is being actively investigated. Calculated hole capture coefficients are in good agreement with experimental data. We discuss the insights gained into the physics of defects in wide-band-gap semiconductors, such as the strength of electron-phonon coupling and the role of different phonon modes.

cond-mat.mtrl-sci↗