arXiv subjects

Sanghamitra Neogi

Publications and source records attributed to Sanghamitra Neogi.

13 recordsLinked to original sources

arXiv2026

A Mean-Field Lindblad Master Equation Framework for Interaction-Driven Decoherence in Solid-State Qubit Ensembles

Multi-qubit systems are essential for scalable quantum technologies, but their performance is often limited by decoherence from qubit--qubit interactions and environmental noise. Although environmental decoherence in single-qubit systems and gate fidelity in multi-qubit systems have been widely studied, a predictive framework connecting qubit interactions, concentration, spatial distribution, and bath occupation to relaxation and decoherence times remains lacking. Here, we develop a multi-qubit mean-field Lindblad master equation (MQMF-LME) framework for the population and coherence dynamics of a solid-state qubit in an interacting multi-qubit environment. The framework treats one qubit as the system of interest and the surrounding qubits as an effective bath, incorporating intrinsic relaxation and bidirectional excitation transfer between the system and the bath. Analytical solutions provide closed-form expressions for density-matrix dynamics, steady-state populations, relaxation time $T_1$, and decoherence time $T_2$, while numerical simulations extend the framework to concentration-dependent dynamics, $1/f$-noise-induced dephasing, and material-specific excitation-transfer mechanisms. For a model system with Förster resonance energy transfer (FRET)-mediated excitation exchange, higher qubit concentrations reduce both $T_1$ and $T_2$, whereas $1/f$ noise reduces $T_2$ without changing $T_1$. Applied to Er$^{3+}$-doped CeO$_2$, the framework shows that long-range FRET-mediated excitation transfer reproduces the experimental decrease in relaxation time with dopant concentration, whereas short-range Dexter-type exchange does not, identifying FRET-mediated excitation transfer as the dominant mechanism. The MQMF-LME framework provides a modular route for linking microscopic interactions and environmental noise sources to measurable decoherence times in solid-state multi-qubit systems.

quant-ph↗

arXiv2026

Bidirectional Learning of Relationships between Atomic Environments and Electronic Band Dispersion in Semiconductor Heterostructures

Atomic-scale variations in semiconductor heterostructures, arising from strain, interfaces, and compositional modulation, strongly influence electronic band dispersion but remain difficult to probe and compare using first-principles methods alone. Here, we introduce a bidirectional learning approach that links local atomic environments to electronic band dispersion using atomically resolved spectral functions as information-dense representations. This formulation enables a forward model that predicts how atomic environments shape electronic bands, and a reverse model that infers atomic-environment descriptors directly from band dispersion images, including angle-resolved photoemission spectra. Applied to silicon/germanium superlattices and heterostructures, the approach reveals how inner and interfacial atomic environments give rise to distinct spectral signatures. The coupled forward-reverse framework enables self-consistent validation by reconstructing electronic band structures from inferred descriptors. By treating electronic bands as decomposable, learnable objects, this work provides a physics-informed route for interpreting spectroscopic data and for data-driven exploration of electronic properties in complex semiconductor heterostructures.

cond-mat.mtrl-sci↗

arXiv2023

Investigation of Phonon Lifetimes and Magnon-Phonon Coupling in YIG/GGG Hybrid Magnonic Systems in the Diffraction Limited Regime

Quantum memories facilitate the storage and retrieval of quantum information for on-chip and long-distance quantum communications. Thus, they play a critical role in quantum information processing and have diverse applications ranging from aerospace to medical imaging fields. Bulk acoustic wave (BAW) phonons are one of the most attractive candidates for quantum memories because of their long lifetime and high operating frequency. In this work, we establish a modeling approach that can be broadly used to design hybrid magnonic high-overtone bulk acoustic wave resonator (HBAR) structures for high-density, long-lasting quantum memories and efficient quantum transduction devices. We illustrate the approach by investigating a hybrid magnonic system, where BAW phonons are excited in a gadolinium iron garnet (GGG) thick film via coupling with magnons in a patterned yttrium iron garnet (YIG) thin film. We present theoretical and numerical analyses of the diffraction-limited BAW phonon lifetimes, modeshapes, and their coupling strengths to magnons in planar and confocal YIG/GGG HBAR structures. We utilize Fourier beam propagation and Hankel transform eigenvalue problem methods and discuss the effectiveness of the two methods to predict the HBAR phonons. We discuss strategies to improve the phonon lifetimes, since increased lifetimes have direct implications on the storage times of quantum states for quantum memory applications. We find that ultra-high, diffraction-limited, cooperativities and phonon lifetimes on the order of ~10^5 and ~10 milliseconds, respectively, could be achieved using a CHBAR structure with 10mum lateral YIG dimension. Additionally, the confocal HBAR structure will offer more than 100-fold improvement of integration density. A high integration density of on-chip memory or transduction centers is naturally desired for high-density memory or transduction devices.

cond-mat.mes-hall↗

arXiv2023

FluxGAN: A Physics-Aware Generative Adversarial Network Model for Generating Microstructures That Maintain Target Heat Flux

We propose a physics-aware generative adversarial network model, FluxGAN, capable of simultaneously generating high-quality images of large microstructures and description of their thermal properties. During the training phase, the model learns about the relationship between the local structural features and the physical processes, such as the heat flux in the microstructures, due to external temperature gradients. Once trained, the model generates new structural and associated heat flux environments, bypassing the computationally expensive modeling. Our model provides a cost effective and efficient approach over conventional modeling techniques, such as the finite element method (FEM), for describing the thermal properties of microstructures. The conventional approach requires computational modeling that scales with the size of the microstructure model, therefore limiting the simulation to a given size, resolution, and complexity of the model. In contrast, the FluxGAN model uses synthesis-by-part approach and generates arbitrary large size images at low computational cost. We demonstrate that the model can be utilized to generate designs of thermal sprayed coatings that satisfies target thermal properties. Furthermore, the model is capable of generating coating microstructures and physical processes in three-dimensional (3D) domain after being trained on two-dimensional (2D) examples. Our approach has the potential to transform the design and optimization of thermal sprayed coatings for various applications, including high-temperature and long-duration operation of gas turbines for aircraft or ground-based power generators.

cond-mat.dis-nn↗

arXiv2021

Effect of Electron-Phonon and Electron-Impurity Scattering on Electronic Transport Properties of Silicon/Germanium Superlattices

Semiconductor superlattices have been extensively investigated for thermoelectric applications, to explore the effects of compositions, interface structures, and lattice strain environments on the reduction of thermal conductivity, and improvement of efficiency. Most studies assumed that their electronic properties remain unaffected compared to their bulk counterparts. However, recent studies demonstrated that electronic properties of silicon (Si)/germanium (Ge) superlattices show significant variations depending on compositions and growth substrates. These studies used a constant relaxation time approximation, and ignored the effects of electron scattering processes. Here, we consider electron scattering with phonons and ionized impurities, and report first-principles calculations of electronic transport properties including the scattering rates. We investigate two classes of Si/Ge superlattices: superlattices with varied compositions grown on identical substrates and with identical compositions but grown on different substrates. We illustrate the relationship between the energy bands of the superlattices and the electron-phonon relaxation times. We model the electron-ionized impurity interaction potentials by accounting for the in-plane and the cross-plane structural anisotropy. Our analysis reveals that the inclusion of electron-phonon and electron-impurity scattering processes can lead to ~1.56-fold improved peak power-factors, compared to that of bulk Si. We observe that superlattices can also display dramatically reduced power-factors for specific lattice strain environments. Such reduction could cancel out thermoelectric efficiency improvements due to reduced thermal conductivities. Our study provides insight to predict variation of electronic properties due to changes in lattice strain environments, essential for designing superlattices with optimized electronic properties.

cond-mat.mtrl-sci↗

arXiv2020

First-Principles Prediction of Electronic Transport in Experimental Semiconductor Heterostructures via Physics-Based Machine Learning

First-principles techniques for electronic transport property prediction have seen rapid progress in recent years. However, it remains a challenge to model heterostructures incorporating variability due to fabrication processes. Machine-learning (ML)-based materials informatics approaches (MI) are increasingly used to accelerate design and discovery of new materials with targeted properties, and extend the applicability of first-principles techniques to larger systems. However, few studies exploited MI to learn electronic structure properties and use the knowledge to predict the respective transport coefficients. In this work, we propose an electronic-transport-informatics (ETI) framework that trains on ab initio models of small systems and predicts thermopower of silicon/germanium heterostructures beyond the length-scale accessible with first-principles techniques, matching measured data. We demonstrate application of MI to extract important physics that determines electronic transport in semiconductor heterostructures, breaking from combinatorial strategies pursued especially for thermoelectric materials. We anticipate that ETI would have broad applicability to diverse materials classes.

cond-mat.mtrl-sci↗

arXiv2019

Theoretical Prediction of Enhanced Thermopower in n-doped Si/Ge Superlattices using Effective Mass Approximation

We analyze the cross-plane miniband transport in n-doped [001] silicon (Si)/germanium (Ge) superlattices using an effective mass approximation (EMA) approach that correctly accounts for the indirect nature of the Si and Ge band gaps. Direct-gap based EMA has been employed so far to investigate the electronic properties of these superlattices, that does not accurately predict transport properties. We use the Boltzmann transport equation framework in combination with the EMA band analysis, and predict that significant improvement of the thermopower of n-doped Si/Ge superlattices can be achieved by controlling the lattice strain environment in these heterostructured materials. We illustrate that a remarkable degree of tunability in the Seebeck coefficient can be attained by growing the superlattices on various substrates, and varying the periods, and the compositions. Our calculations show up to ~3.2-fold Seebeck enhancement in Si/Ge [001] superlattices over bulk silicon, in the high-doping regime, breaking the Pisarenko relation. The thermopower modulations lead to an increase of power factor by up to 20%. Our approach is generically applicable to other superlattice systems, e.g., to investigate dimensional effects on electronic transport in two-dimensional nanowire and three dimensional nanodot superlattices. A material with high S potentially improves the energy conversion efficiency of thermoelectric applications and additionally, is highly valuable in various Seebeck metrology techniques including thermal, flow, radiation, and chemical sensing applications. We anticipate that the ideas presented here will have a strong impact in controlling electronic transport in various thermoelectric, opto-electronic, and quantum-enhanced materials applications.

cond-mat.mtrl-sci↗

arXiv2019

Anisotropic In-Plane Phonon Transport in Ultrathin Silicon Membranes Guided by Nano-Surface-Resonators

Anisotropic phonon transport along different lattice directions of two-dimensional (2D) materials has been observed, however, the effect decreases with increasing the thickness beyond a few atomic layers. Here we establish a novel mechanism to induce anisotropic phonon transport in quasi-2D materials with isotropic symmetry. The phonon propagation is guided by resonance hybridization with surface nanostructures. We demonstrate that the thermal conductivity of 3 nm-thick silicon membrane with surface nanofins is greater by $\sim50\%$ parallel to the fins than that perpendicular to the fins.

cond-mat.mes-hall↗

arXiv2019

Modulation of Semiconductor Superlattice Thermopower Through Symmetry and Strain

In doped semiconductors and metals, the thermopower decreases with increasing carrier concentration, in agreement with the Pisarenko relation. Here, we demonstrate a new strain engineering approach to increase the thermopower of [001] Si/Ge superlattices (SLs) beyond this relation. Using two independent theoretical modeling approaches, we show that new bands form due to the structural symmetry, and, the SL bands are highly tunable with epitaxial substrate strain. The band shifts lead to a modulated thermopower, with a peak $\sim$5-fold enhancement in strained Si/Ge SLs in the high doping regime.

cond-mat.mtrl-sci↗

arXiv2018

Heat and charge transport in bulk semiconductors with interstitial defects

Interstitial defects are inevitably present in doped semiconductors that enable modern-day electronic, optoelectronic or thermoelectric technologies. Understanding of stability of interstitials and their bonding mechanisms in the silicon lattice was accomplished only recently with the advent of first-principles modeling techniques, supported by powerful experimental methods. However, much less attention has been paid to the effect of different naturally occurring interstitials on the thermal and electrical properties of silicon. In this work, we present a systematic study of the variability of heat and charge transport properties of bulk silicon, in the presence of randomly distributed interstitial defects (Si, Ge, C and Li). We find through atomistic lattice dynamics and molecular dynamics modeling studies that, interstitial defects scatter heat-carrying phonons to suppress thermal transport-1.56% of randomly distributed Ge and Li interstitials reduce the thermal conductivity of silicon by $\sim$ 30 and 34 times, respectively. Using first principles density functional theory and semi-classical Boltzmann transport theory, we compute electronic transport coefficients of bulk Si with 1.56% Ge, C, Si and Li interstitials, in hexagonal, tetrahedral, split-interstitial and bond-centered sites. We demonstrate that hexagonal-Si and hexagonal-Ge interstitials minimally impact charge transport. To complete the study, we predict the thermoelectric property of an experimentally realizable bulk Si sample that contains Ge interstitials in different symmetry sites. Our research establishes a direct relationship between the variability of structures dictated by fabrication processes and heat and charge transport properties of silicon. The relationship provides guidance to accurately estimate performance of Si-based materials for various technological applications.

cond-mat.mtrl-sci↗

arXiv2017

Native surface oxide turns alloyed silicon membranes into nanophononic metamaterials with ultra-low thermal conductivity

A detailed understanding of the relation between microscopic structure and phonon propagation at the nan oscale is essential to design materials with desired phononic and thermal properties.Here we uncover a new mechanism of phonon interaction in surface oxidized membranes, i.e., native oxide layers interact with phonons in ultra-thin silicon membranes through local resonances. The local resonances reduce the low frequency phonon group velocities and shorten their mean free path. This effect opens up a new strategy for ultralow thermal conductivity design as it complements the scattering mechanism which scatters higher frequency modes effectively. The combination of native oxide layer and alloying with germanium in concentration as small as 5% reduces the thermal conductivity of silicon membranes to 100 time lower than the bulk. In addition, the resonance mechanism produced by native oxide surface layers is particularly effective for thermal condutivity reduction even at very low temperatures, at which only low frequency modes are populated.

cond-mat.mtrl-sci↗

arXiv2016

Lattice dynamics model calculation of Kapitza conductance at solid-fluid interfaces

Existing theoretical models of the interfacial thermal conductance, i.e., Kapitza conductance, of insulating solid-fluid interfaces only consider bulk properties, e.g., acoustic mismatch model and diffuse mismatch model. In this work, we propose a classical lattice dynamical model calculation of the Kapitza conductance, thereby incorporating interfacial structural details. In our model, we assume that heat is mostly carried by phonons in the solid, and that sound waves carry diffusive heat from the interface into the bulk of the liquid, where both longitudinal and transverse sound waves are considered. Sound wave dispersion is calculated from the fluid pair distribution function, evaluated using approximate integral equation theories (i.e., Percus-Yevick, Hypernetted-chain approximation). The Kapitza conductance of the solid-fluid interface is obtained from the phonon transmission coefficient at the interface. We determine the interfacial phonon transmission coefficient by solving the coupled equations of motion for the interfacial solid and fluid atoms. As an illustrative example, we derive the Kapitza conductance of solid argon-fluid neon interface, with pair-wise Lennard-Jones interactions.

cond-mat.mtrl-sci↗

arXiv2009

Pair Distribution Function of a Square-Well Fluid

The properties of the ground state of liquid $^4$He are studied using a correlated basis function of the form $\prod_{i<j} ψ(r_{ij})$. Here, $ψ(r)$ is chosen as the exact solution of the Schrödinger equation for two $^4$He atoms. A hard-sphere plus an attractive square well is used as the interaction potential between $^4$He atoms. The pair distribution function is calculated using approximate integral methods, namely the Percus-Yevick (PY) equation and Hypernetted Chain (HNC) approximation. The values thus obtained are used to calculate the ground state energy, which is found to be -4.886 K using the PY equation. The liquid structure factor is also obtained using the pair distribution function. The values for the pair distribution function and liquid structure factor are compared with experimental results and earlier theoretical calculations.

cond-mat.other↗