arXiv subjects

Vivek Chowdhury

Publications and source records attributed to Vivek Chowdhury.

5 recordsLinked to original sources

arXiv2026

Machine learning based prediction of optical properties in two-dimensional Mo-W-S-Se-Te transition-metal dichalcogenide alloys through physics-informed sampling

Two-dimensional transition-metal dichalcogenide (TMD) alloys provide a compositionally tunable platform for controlling the optical and electronic properties. However, systematic prediction of their dielectric response across multicomponent alloy spaces remains challenging owing to the combinatorial cost of first-principles calculations. In this work, we combined ab initio optical-property calculations with a tabular foundation-model regression to predict the real and imaginary components of the frequency-dependent dielectric function for Mo-W-S-Se-Te TMD alloys. A dataset of 99 alloy structures spanning binary, ternary, quaternary, and quinary compositions was generated using density functional theory (DFT). The resulting polarization-dependent dielectric spectra were used to train a tabular prior-fitted network (TabPFN) and evaluated against the conventional Extra Trees and XGBoost models. To accommodate the in-context capacity limit of TabPFN, we introduced a non-uniform, physics-informed energy subsampling strategy that concentrates sampling in the optically active region above the band gap, where interband absorption is strongest. Trained solely on quaternary alloys, our TabPFN reconstructed the dielectric spectra of held-out quaternary compositions with an R2 > 0.98 and a mean absolute error below 0.10 for all four dielectric components, outperforming both baselines while requiring no gradient-based training or hyperparameter tuning. Our model further predicted derived optical quantities, including refractive index, extinction coefficient, and absorption coefficient. Additionally, our model generalized in a zero-shot manner to binary, ternary, and quinary alloys absent from the training set, with quinary predictions achieving an R2 > 0.97.

cond-mat.mtrl-sci↗

arXiv2026

Strain- and Electric-Field-Tunable Valley Polarization in Mo0.75V0.25Te2(Mo3VTe8) for Valleytronic Application

Valley polarization in 2D TMDs is promising for low-power valleytronic and spin-valley information processing, but time-reversal symmetry in pristine nonmagnetic TMDs keeps the K+ and K- valleys degenerate, limiting device applications. In this work, we investigated the structural stability, electronic properties, and tunable valley polarization of V-alloyed MoTe2 monolayer, Mo0.75V0.25Te2, using first-principles density functional theory (DFT) calculations. Substitutional alloying of MoTe2 with V introduced magnetic exchange interaction, which, together with spin-orbit coupling (SOC), lifted the valley degeneracy at the unequal valleys. The alloyed structure was found to be energetically and dynamically stable due to the absence of imaginary phonon modes. In pristine MoTe2, SOC produced spin splittings of 34.0 meV and 218.9 meV in the conduction bands and valence bands, respectively, but no valley polarization was observed. In contrast, Mo0.75V0.25Te2 exhibited spontaneous valley polarization of 37.3 meV in the conduction band and 78.2 meV in the valence band. The valley polarization was further enhanced by external electric fields and biaxial strain. A transverse electric field along the crystal c axis produced the maximum valley splitting of 132.8 meV in the valence band, whereas biaxial tensile strain increased the valence band valley splitting up to 160.8 meV. The maximum conduction band valley splitting reached 54.4 meV under 2% biaxial compressive strain. These results demonstrated that V alloying, combined with electric-field and strain engineering, provides an effective strategy for achieving large and tunable valley polarization in MoTe2. Thus, Mo0.75V0.25Te2 can be considered a promising 2D platform for tunable valleytronic device applications, such as transistors and sensors.

cond-mat.mtrl-sci↗

arXiv2026

Robust Spin Splitting and Strain-Controlled Optical Response in Monolayer CrC2N4 for Valleytronic and Optoelectronic Applications

Monolayer CrC2N4 recently emerged as a promising two-dimensional semiconductor, yet its spin-orbit-coupled (SOC) physics and strain-tunable optical response remained largely unexplored. Here, we investigated the electronic, valley, charge-transfer, and optical properties of pristine and biaxially strained monolayer CrC2N4 using first-principles calculations. The monolayer exhibited a direct band gap at the K/K' valleys. SOC produced valley contrasting out-of-plane spin polarization, yielding a moderate valence band spin splitting of 51.9 meV and a small conduction band spin splitting of 1.7 meV. Orbital-resolved analysis showed that the edge states were mainly governed by Cr-d and N-p hybridization, while Bader analysis indicated polar-covalent bonding through charge transfer toward N atoms. Biaxial strain in the range of -4% to +4% tuned the band gap from 1.987 to 1.421 eV and drove an indirect-to-direct gap transition near -1% strain. Tensile strain enhanced the Berry curvature and red-shifted the optical response toward the visible-near-infrared region. These results suggested monolayer CrC2N4 as a promising platform for strain-engineered valleytronic and optoelectronic device applications.

cond-mat.mtrl-sci↗

arXiv2025

Strain-Tunable Spin Filtering and Valley Splitting Coexisting with Anomalous Hall Effect in 2D Half-Metallic VSe2/VN Heterostructure: Toward a Unified Spintronic-Valleytronic Platform

Rapid progress in valleytronics and spintronics is limited by the scarcity of two-dimensional materials that simultaneously provide robust valley splitting and strong spin selectivity. Here we showed that a van der Waals heterostructure (VSe2/VN) built from hexagonal VSe2 and hexagonal VN addressed this gap. Using first-principles density functional theory, phonon, ab initio molecular dynamics stability tests, Bader charge analysis, and Wannier-based Berry-curvature calculations, we demonstrated an energetically and dynamically stable heterostructure that exhibited interlayer charge transfer and a work function intermediate between the constituent monolayers. The electronic structure showed small indirect PBE gap (108.9 meV), with HSE06 indicating a half-metallic tendency; a sizable conduction-band valley splitting (ΔCKK' = 22.9 meV for spin-up and ΔCKK' = 61.3 meV for spin-down); and pronounced spin asymmetry, where the spin-down channel showed a wide semiconducting gap (0.64 eV) while the spin-up channel was nearly gapless. These features yielded a high zero-strain spin-filter efficiency P = 75.4%, tunable to 82.5% under +4% biaxial tensile strain. The heterostructure also supported non-zero, valley-contrasting Berry curvature, and a large anomalous Hall conductivity (peak sigmaxy = 568.33 S/cm). Importantly, mean-field estimation placed the ferromagnetic Curie temperature near room temperature at zero strain (Tc = 284.04 K), while Tc decreased to 183.9 K at +4% strain, the magnetic order remained robust to cryogenic temperatures, providing a beneficial tuning knob to balance spin-filter performance with thermal stability in device-relevant regimes. These results identified VSe2/VN as a practical, strain-tunable platform for integrated valleytronic, spintronic devices, and for exploring anomalous Hall and valley-dependent transport phenomena.

cond-mat.mtrl-sci↗

arXiv2025

Load Forecasting on A Highly Sparse Electrical Load Dataset Using Gaussian Interpolation

Sparsity, defined as the presence of missing or zero values in a dataset, often poses a major challenge while operating on real-life datasets. Sparsity in features or target data of the training dataset can be handled using various interpolation methods, such as linear or polynomial interpolation, spline, moving average, or can be simply imputed. Interpolation methods usually perform well with Strict Sense Stationary (SSS) data. In this study, we show that an approximately 62\% sparse dataset with hourly load data of a power plant can be utilized for load forecasting assuming the data is Wide Sense Stationary (WSS), if augmented with Gaussian interpolation. More specifically, we perform statistical analysis on the data, and train multiple machine learning and deep learning models on the dataset. By comparing the performance of these models, we empirically demonstrate that Gaussian interpolation is a suitable option for dealing with load forecasting problems. Additionally, we demonstrate that Long Short-term Memory (LSTM)-based neural network model offers the best performance among a diverse set of classical and neural network-based models.

cs.LG↗