arXiv subjects

Nguyen Tuan Hung

Publications and source records attributed to Nguyen Tuan Hung.

17 recordsLinked to original sources

arXiv2026

Anharmonic Phonon Renormalization and Defect Tolerance of the Thermoelectric Power Factor in Monolayer SnSe

Monolayer tin selenide (SnSe) exhibits phase-dependent anharmonic lattice dynamics, yet their consequences for the thermoelectric power factor (PF) and point-defect tolerance remain unresolved. We combine density functional theory, the stochastic self-consistent harmonic approximation (SSCHA), and Boltzmann transport calculations including electron-phonon and electron-defect scattering to investigate monolayer $α$-SnSe (Pnma) and $β$-SnSe (Cmcm). In dynamically stable $α$-SnSe, SSCHA renormalizes the finite-temperature phonons without changing the qualitative n-type transport picture. In $β$-SnSe, SSCHA removes the harmonic soft-mode instability of the Cmcm phase at 800-1000 K, and thereby enables high-temperature transport calculations; LO/TO-2 is the principal electron-scattering channel. In the lower-density window near $10^{12}$ cm$^{-2}$, the n-type PF reaches 15-19 $μ\mathrm{W}/(\mathrm{K}^{2}\cdot\mathrm{cm})$ at 800-900 K and exceeds the p-type PF primarily because of the higher electrical conductivity. Se vacancies ($V_{\mathrm{Se}}$) produce weaker electron-defect scattering than Sn vacancies ($V_{\mathrm{Sn}}$), and p-type transport is less defect tolerant than n-type transport in both phases. We define an operational critical defect concentration, $C_{\mathrm{crit}}$, at which the PF decreases by 15% relative to the corresponding defect-free value. The lowest $C_{\mathrm{crit}}$ is $8.841\times10^{-5}$ (approximately 88 ppm) for p-type $α$-SnSe with $V_{\mathrm{Sn}}$; for n-type $β$-SnSe with $V_{\mathrm{Se}}$, the 15% threshold is not reached up to $5\times10^{-3}$ (5000 ppm). These results distinguish finite-temperature phonon renormalization in stable $α$-SnSe from anharmonic stabilization in $β$-SnSe and provide defect-concentration limits for preserving the PF.

cond-mat.mtrl-sci↗

arXiv2026

Tunable Rashba Splitting in Janus InXPbP (X = S, Se, Te) Monolayers for Enhanced Photocatalytic Water Splitting

Janus two-dimensional (2D) materials exhibiting Rashba spin splitting have recently attracted considerable attention owing to their potential applications in spintronic devices and photocatalytic water splitting. In this work, we investigate, using first-principles calculations, the structural, mechanical, electronic, optical, and photocatalytic properties of Janus InXPbP (X = S, Se, Te) monolayers that exhibit significant Rashba effects. Our results demonstrate that all three monolayers are energetically, dynamically, and mechanically stable, as evidenced by cohesive energy calculations, phonon dispersion analysis, and elastic constants. By varying the chalcogen atom (X = S, Se, Te), the Rashba effect in InXPbP can be effectively tuned. Rashba parameters of 0.16 and 0.20 eVÅ are obtained near the conduction-band minimum (CBM) for InSPbP and InSePbP, respectively, whereas InTePbP exhibits giant Rashba spin splitting near both the CBM and valence-band maximum (VBM), with corresponding Rashba parameters of 0.90 and 0.87 eVÅ. Furthermore, the Janus InXPbP monolayers exhibit suitable band gaps of 1.21, 1.27, and 0.76 eV for InSPbP, InSePbP, and InTePbP, respectively, which are favorable for photocatalytic applications. All three monolayers possess suitable band-edge alignments for overall water splitting, yielding solar-to-hydrogen (STH) conversion efficiencies of 21.67%, 26.03%, and 29.83% for InSPbP, InSePbP, and InTePbP, respectively. Our findings not only enrich the family of Janus materials but also suggest that the Janus InXPbP monolayers are promising candidates for spintronic devices and high-performance photocatalytic water-splitting applications.

cond-mat.mtrl-sci↗

arXiv2026

Suppression of Metallic Transport in Nitrogen-rich Two-Dimensional Transition Metal Nitrides

The recent experimental realization of two-dimensional (2D) transition metal nitrides (TMNs, e.g., Mo5N6, δ-MoN, and W5N6) opens new opportunities for exploring their fundamental physical properties at the two-dimensional limit. In this work, we propose a unified picture of transport phenomena in the nitrogen-rich 2D W5N6 and Mo5N6, and the stoichiometric 2D δ-MoN based on several observations and first-principles calculations. Temperature coefficient of resistance (TCR) and magnetoresistance (MR) from Hall measurements consistently suggest disorder-induced transport mechanism at low temperatures (10-30 K). Notably, we observe a transition from metal to semimetal driven by the variation of nitrogen content in TMNs, supported by the suppressed density of states at the Fermi energy in nitrogen-rich TMNs (e.g. Mo5N6) from first-principle calculations. Carrier density calculations of bulk TMNs and 2D TMNs with -NH termination groups further reveal the switching of majority carrier type of Mo5N6 at reduced thickness, which is in great agreement with Hall measurement results. Our findings demonstrate that high nitrogen content in metallic molybdenum nitrides can induce the transition to a semimetallic phase at the 2D limit, shedding light on both the fundamental aspects of these materials and directions in future material design.

cond-mat.mtrl-sci↗

arXiv2025

Rapid Machine Learning-Driven Detection of Pesticides and Dyes Using Raman Spectroscopy

The extensive use of pesticides and synthetic dyes poses critical threats to food safety, human health, and environmental sustainability, necessitating rapid and reliable detection methods. Raman spectroscopy offers molecularly specific fingerprints but suffers from spectral noise, fluorescence background, and band overlap, limiting its real-world applicability. Here, we propose a deep learning framework based on ResNet-18 feature extraction, combined with advanced classifiers, including XGBoost, SVM, and their hybrid integration, to detect pesticides and dyes from Raman spectroscopy, called MLRaman. The MLRaman with the CNN-XGBoost model achieved a predictive accuracy of 97.4% and a perfect AUC of 1.0, while it with the CNN-SVM model provided competitive results with robust class-wise discrimination. Dimensionality reduction analyses (PCA, t-SNE, UMAP) confirmed the separability of Raman embeddings across 10 analytes, including 7 pesticides and 3 dyes. Finally, we developed a user-friendly Streamlit application for real-time prediction, which successfully identified unseen Raman spectra from our independent experiments and also literature sources, underscoring strong generalization capacity. This study establishes a scalable, practical MLRaman model for multi-residue contaminant monitoring, with significant potential for deployment in food safety and environmental surveillance.

cond-mat.mtrl-sci↗

arXiv2025

Point Defects Limited Carrier Mobility in Janus MoSSe monolayer

Point defects, often formed during the growth of Janus MoSSe, act as built-in scatterers and affect carrier transport in electronic devices based on Janus MoSSe. In this study, we employ first-principles calculations to investigate the impact of common defects, such as sulfur vacancies, selenium vacancies, and chalcogen substitutions, on electron transport, and compare their influence with that of mobility limited by phonons. Here, we define the saturation defect concentration ($C_{\mathrm{sat}}$) as the highest defect density that still allows the total mobility to remain within 90\% of the phonon-limited value, providing a direct measure of how many defects a device can tolerate. Based on $C_{\mathrm{sat}}$, we find a clear ranking of defect impact: selenium substituting for sulfur is relatively tolerant, with $C_{\mathrm{sat}}\approx2.07\times10^{-4}$, while selenium vacancies are the most sensitive, with $C_{\mathrm{sat}}\approx3.65\times10^{-5}$. Our $C_{\mathrm{sat}}$ benchmarks and defect hierarchy provide quantitative, materials-specific design rules that can guide the fabrication of high-mobility field-effect transistors, electronic devices, and sensors based on Janus MoSSe.

cond-mat.mtrl-sci↗

arXiv2025

Strain effect on optical properties and quantum weight of monolayer MnBi$_2$X$_4$ (X = Te, Se, S)

Manipulating the optical and quantum properties of two-dimensional (2D) materials through strain engineering is not only fundamentally interesting but also provides significant benefits across various applications. In this work, we employ first-principles calculations to investigate the effects of strain on the magnetic and optical properties of the monolayer MnBi$_2$X$_4$ (X = Te, Se, S). Our results indicate that biaxial strain enhances the Mn magnetic moment, while uniaxial strains reduce it. Significantly, the strain-dependent behavior, quantified through the quantum weight, can be leveraged to control the system's quantum geometry and topological features. Particularly, uniaxial strains reduce the quantum weight and introduce anisotropy, thus providing an additional degree of freedom to tailor device functionalities. Finally, by analyzing chemical bonds under various strain directions, we elucidate how the intrinsic ductile or brittle fracture behavior of MnBi$_2$X$_4$ could impact fabrication protocols and structural stability. These insights pave the way for strain-based approaches to optimize the quantum properties in 2D magnetic topological insulators in practical device contexts.

cond-mat.mtrl-sci↗

arXiv2025

QR$^2$-code: An open-source program for double resonance Raman spectra

We present an open-source program, QR$^2$-code, that computes double-resonance Raman (DRR) spectra using first-principles calculations. QR$^2$-code can calculate not only two-phonon DRR spectra but also single-resonance Raman spectra and defect-induced DRR spectra. For defect-induced DDR spectra, we simply assume that the electron-defect matrix element of elastic scattering is a constant. Hands-on tutorials for graphene are given to show how to run QR$^2$-code for single-resonance, double-resonance, and defect-induced Raman spectra. We also compare the single-resonance Raman spectra by QR$^2$-code with that by QERaman code. In QR$^2$-code, the energy dispersions of electron and phonon are taken from Quantum ESPRESSO (QE) code, and the electron-phonon matrix element is obtained from the electron-phonon Wannier (EPW) code. All codes, examples, and scripts are available on the GitHub repository.

cond-mat.mtrl-sci↗

arXiv2025

Strain Effect on Rashba Splitting and Phonon Scattering to Improve Thermoelectric Performance of 2D Heterobilayer MoTe$_{2}$/PtS$_{2}$

Rashba spin-orbit coupling significantly modifies the electronic band structure in two-dimensional (2D) van der Waals (vdW) heterobilayers, which may enhance their thermoelectric (TE) properties. In this study, we use first-principles calculations and Boltzmann transport theory to explore the strain effect on the TE performance of the 2D vdW heterobilayer MoTe$_{2}$/PtS$_{2}$. A strong Rashba spin-splitting is observed in the valence band, resulting in an increase in the Seebeck coefficient for p-type. The lattice thermal conductivity of MoTe$_{2}$/PtS$_{2}$ is remarkably low about of 0.6 Wm$^{-1}$K$^{-1}$ at $T = 300$ K due to large anharmonic scattering. Furthermore, biaxial strain enhances the power factor (PF) by introducing band convergence. At a strain of 2\%, the optimal PF for the n-type material reaches 170 $μ$W/cmK$^{2}$, indicating approximately 84.78\% increase compared to the unstrained state (92 $μ$W/cmK$^{2}$). Given the low lattice thermal conductivity, the optimized figure of merit $ZT$ achieves up to 0.88 at 900 K for n-type. Our findings indicate that MoTe$_{2}$/PtS$_{2}$ is a highly promising candidate for 2D heterobilayer TE materials, owing to its strong Rashba splitting and significant anharmonicity.

cond-mat.mtrl-sci↗

arXiv2025

AI-Driven Defect Engineering for Advanced Thermoelectric Materials

Thermoelectric materials offer a promising pathway to directly convert waste heat to electricity. However, achieving high performance remains challenging due to intrinsic trade-offs between electrical conductivity, the Seebeck coefficient, and thermal conductivity, which are further complicated by the presence of defects. This review explores how artificial intelligence (AI) and machine learning (ML) are transforming thermoelectric materials design. Advanced ML approaches including deep neural networks, graph-based models, and transformer architectures, integrated with high-throughput simulations and growing databases, effectively capture structure-property relationships in a complex multiscale defect space and overcome the curse of dimensionality. This review discusses AI-enhanced defect engineering strategies such as composition optimization, entropy and dislocation engineering, and grain boundary design, along with emerging inverse design techniques for generating materials with targeted properties. Finally, it outlines future opportunities in novel physics mechanisms and sustainability, highlighting the critical role of AI in accelerating the discovery of thermoelectric materials.

cond-mat.mtrl-sci↗

arXiv2025

AI-driven materials design: a mini-review

Materials design is an important component of modern science and technology, yet traditional approaches rely heavily on trial-and-error and can be inefficient. Computational techniques, enhanced by modern artificial intelligence (AI), have greatly accelerated the design of new materials. Among these approaches, inverse design has shown great promise in designing materials that meet specific property requirements. In this mini-review, we summarize key computational advancements for materials design over the past few decades. We follow the evolution of relevant materials design techniques, from high-throughput forward machine learning (ML) methods and evolutionary algorithms, to advanced AI strategies like reinforcement learning (RL) and deep generative models. We highlight the paradigm shift from conventional screening approaches to inverse generation driven by deep generative models. Finally, we discuss current challenges and future perspectives of materials inverse design. This review may serve as a brief guide to the approaches, progress, and outlook of designing future functional materials with technological relevance.

cond-mat.mtrl-sci↗

arXiv2025

Rational Design Heterobilayers Photocatalysts for Efficient Water Splitting Based on 2D Transition-Metal Dichalcogenide and Their Janus

Direct Z-scheme heterobilayers with enhanced redox potential are viewed as promising for solar-driven water splitting, arising from the synergy between intrinsic dipoles in Janus materials and interfacial electric fields across the layers. This study explores 20 two-dimensional Janus transition-metal dichalcogenide (TMDC) heterobilayers for efficient water splitting. Using density-functional theory (DFT) calculations, we screen them based on band gaps and intrinsic electric fields to identify promising candidates, then further assess carrier mobility and surface chemistry to fully evaluate their overall performance. By examining the alignment of synthetic and internal electric fields, we distinguish between Type-I, Type-II, and Z-scheme configurations, enabling the targeted design of optimal photocatalytic materials. Furthermore, we employ the Fröhlich interaction model to quantify the mobility contributions from the longitudinal optical phonon mode, providing detailed insights into how carrier mobility, influenced by phonon scattering, affects photocatalytic performance. Our findings demonstrate the potential of Janus-based Z-scheme systems to overcome existing limitations in photocatalytic water splitting by optimizing the electronic and structural properties of 2D materials, highlighting a viable pathway for advancing clean energy generation through enhanced photocatalytic processes.

cond-mat.mtrl-sci↗

arXiv2024

Downscaling of non van der Waals Semimetallic W5N6 with Resistivity Preservation

The bulk phase of transition metal nitrides (TMNs) has long been a subject of extensive investigation due to their utility as coating materials, electrocatalysts, and diffusion barriers, attributed to their high conductivity and refractory properties. Downscaling TMNs into two-dimensional (2D) forms would provide valuable members to the existing 2D materials repertoire, with potential enhancements across various applications. Moreover, calculations have anticipated the emergence of uncommon physical phenomena in TMNs at the 2D limit. In this study, we use the atomic substitution approach to synthesize 2D W5N6 with tunable thicknesses from tens of nanometers down to 2.9 nm. The obtained flakes exhibit high crystallinity and smooth surfaces. Electrical measurements on 15 samples show an average electrical conductivity of 161.1 S/cm, which persists while thickness decreases from 45.6 nm to 2.9 nm. The observed weak gate tuning effect suggests the semimetallic nature of the synthesized 2D W5N6. Further investigation into the conversion mechanism elucidates the crucial role of chalcogen vacancies in the precursor for initiating the reaction and strain in propagating the conversion. Our work introduces a desired semimetallic crystal to the 2D material library with mechanistic insights for future design of the synthesis.

cond-mat.mtrl-sci↗

arXiv2024

Structural Constraint Integration in Generative Model for Discovery of Quantum Material Candidates

Billions of organic molecules are known, but only a tiny fraction of the functional inorganic materials have been discovered, a particularly relevant problem to the community searching for new quantum materials. Recent advancements in machine-learning-based generative models, particularly diffusion models, show great promise for generating new, stable materials. However, integrating geometric patterns into materials generation remains a challenge. Here, we introduce Structural Constraint Integration in the GENerative model (SCIGEN). Our approach can modify any trained generative diffusion model by strategic masking of the denoised structure with a diffused constrained structure prior to each diffusion step to steer the generation toward constrained outputs. Furthermore, we mathematically prove that SCIGEN effectively performs conditional sampling from the original distribution, which is crucial for generating stable constrained materials. We generate eight million compounds using Archimedean lattices as prototype constraints, with over 10% surviving a multi-staged stability pre-screening. High-throughput density functional theory (DFT) on 26,000 survived compounds shows that over 50% passed structural optimization at the DFT level. Since the properties of quantum materials are closely related to geometric patterns, our results indicate that SCIGEN provides a general framework for generating quantum materials candidates.

cond-mat.mtrl-sci↗

arXiv2024

Ensemble-Embedding Graph Neural Network for Direct Prediction of Optical Spectra from Crystal Structure

Optical properties in solids, such as refractive index and absorption, hold vast applications ranging from solar panels to sensors, photodetectors, and transparent displays. However, first-principles computation of optical properties from crystal structures is a complex task due to the high convergence criteria and computational cost. Recent progress in machine learning shows promise in predicting material properties, yet predicting optical properties from crystal structures remains challenging due to the lack of efficient atomic embeddings. Here, we introduce GNNOpt, an equivariance graph-neural-network architecture featuring automatic embedding optimization. This enables high-quality optical predictions with a dataset of only 944 materials. GNNOpt predicts all optical properties based on the Kramers-Kr{ö}nig relations, including absorption coefficient, complex dielectric function, complex refractive index, and reflectance. We apply the trained model to screen photovoltaic materials based on spectroscopic limited maximum efficiency and search for quantum materials based on quantum weight. First-principles calculations validate the efficacy of the GNNOpt model, demonstrating excellent agreement in predicting the optical spectra of unseen materials. The discovery of new quantum materials with high predicted quantum weight, such as SiOs which hosts exotic quasiparticles, demonstrates GNNOpt's potential in predicting optical properties across a broad range of materials and applications.

cond-mat.mtrl-sci↗

arXiv2023

The role of spin-orbit interaction in low thermal conductivity of Mg$_3$Bi$_2$

Three-dimensional layered Mg$_3$Bi$_2$ has emerged as thermoelectric material due to its high cooling performance at ambient temperature, which benefits from its low lattice thermal conductivity and semimetal character. However, the semimetal character of Mg$_3$Bi$_2$ is sensitive to spin-orbit coupling (SOC). Thus, the underlying origin of low lattice thermal conductivity needs to be clarified in the presence of the SOC. In this work, the first-principles calculations within the two-channel model are employed to investigate the effects of the SOC on the phonon-phonon scattering on the phonon transport of Mg$_3$Bi$_2$. Our results show that the SOC strongly reduces the lattice thermal conductivity (up to $\sim 35$ %). This reduction originates from the influence of the SOC on the transverse acoustic modes involving interlayer shearing, leading to weak interlayer bonding and enhancement anharmonicity around 50 cm$^{-1}$. Our results clarify the mechanism of low thermal conductivity in Mg$_3$Bi$_2$ and support the design of Mg$_3$Bi$_2$-based materials for thermoelectric applications.

cond-mat.mtrl-sci↗

arXiv2019

Tunable Circular Dichroism and Valley Polarization in the Modified Haldane Model

We study the polarization dependence of optical absorption for the modified Haldane model, which exhibits antichiral edge modes in presence of sample boundaries and has been argued to be realizable in transition metal dichalcogenides or Weyl semimetals. A rich optical phase diagram is unveiled, in which the correlations between perfect circular dichroism, pseudospin and valley polarization can be tuned independently upon varying the Fermi energy. Importantly, perfect circular dichroism and valley polarization are achieved simultaneously. This unprecedented combination of optical properties suggests some interesting novel photonic device functionality (e.g. light polarizer) which could be combined with valleytronics applications (e.g. generation of valley currents).

cond-mat.mes-hall↗

arXiv2016

Intrinsic strength and failure behaviors of ultra-small single-walled carbon nanotubes

The intrinsic mechanical strength of single-walled carbon nanotubes (SWNTs) within the diameter range of 0.3-0.8 nm has been studied based on ab initio density functional theory calculations. In contrast to predicting "smaller is stronger and more elastic" in nanomaterials, the strength of the SWNTs is significantly reduced when decreasing the tube diameter. The results obtained show that the Young`s modulus E significantly reduced in the ultra-small SWNTs with the diameter less than 0.4 nm originates from their very large curvature effect, while it is a constant of about 1.0 TPa, and independent of the diameter and chiral index for the large tube. We find that the Poisson`s ratio, ideal strength and ideal strain are dependent on the diameter and chiral index. Furthermore, the relations between E and ideal strength indicate that Griffith`s estimate of brittle fracture could break down in the smallest (2, 2) nanotube, with the breaking strength of 15% of E. Our results provide important insights into intrinsic mechanical behavior of ultra-small SWNTs under their curvature effect.

cond-mat.mtrl-sci↗