arXiv subjects

Kunal Mukherjee

Publications and source records attributed to Kunal Mukherjee.

At least 19 recordsLinked to original sources

arXiv2026

Nonresonant optomechanical control of structural phases

Optical tweezers demonstrate how light can exert forces to trap, repel, and manipulate microscopic particles without absorption. Recent theory has suggested that such forces can extend beyond particle manipulation to drive structural phase transitions in solids. Here we apply this optomechanical principle to tin selenide (SnSe), a material where proximity to several different structural phases gives rise to its high thermoelectric figure of merit and makes it a candidate for a switchable topological crystalline insulator. Whereas the force for standard optical tweezers arises from a gradient in the intensity of a light field, the optomechanical force is mediated by a gradient in the dielectric constant as a function of phonon coordinate. Unlike conventional methods that rely on resonant excitation and absorption through the imaginary part of the dielectric function, this approach operates dispersively through the real part and can be directly driven by Raman processes, enabling selective transitions with reduced energy cost and ultrafast response. Using time-domain Raman scattering, we show that above a critical mid-infrared field strength the $A_g$ Raman modes disappear abruptly without softening, signaling the formation of a new structural phase. This phase, distinct from those induced by heating or carrier excitation, exhibits large-amplitude and long-lived modulations in its optical response. Complementing this observation, we show also evidence for an equivalent DC-field-driven structural phase transformation to a higher symmetry phase, as observed by atom probe tomography. Our study demonstrates the concept of nonresonant optomechanical phase control and defines novel opportunities for synthesizing hidden structural phases with unique functional properties.

physics.optics↗

arXiv2026

Monolithic integration of optically anisotropic GeSe-based films on GaAs by templated solid-phase epitaxy

Layered IV-VI semiconductors such as GeSe exhibit strong in-plane optical anisotropy, making them promising candidates for polarization-sensitive photonic devices. However, realizing these properties in scalable platforms requires heteroepitaxial integration on technologically relevant substrates like GaAs. Direct growth of GeSe is complicated by its glass formation at low temperatures and high vapor pressure at elevated temperatures. To overcome this, we develop a method for ex-situ solid-phase epitaxy utilizing a SnSe buffer and offcut GaAs substrate to enable single-orientation crystalline GeSe films. Using polarized reflection measurements, we find that stabilizing a single-in-plane-orientation results in a 2x increase in anisotropic response between the armchair and zigzag directions. This work provides a new integration route to harness the anisotropic optical properties of GeSe and its alloys for polarization-sensitive technologies.

cond-mat.mtrl-sci↗

arXiv2026

Spaces of UCP maps and subalgebras of von Neumann algebras

In this paper, we establish that several natural topologies on the space of state-preserving unital completely positive maps coincide and that make it a Polish space. We then focus on the subspace of state-preserving conditional expectations and analyse its topology in detail, recovering the Haagerup-Winslow result that it aligns with the Effros-Mar\'echal topology on the space of von Neumann subalgebras. This correspondence is then applied to structural classes of subalgebras, including amenable, Haagerup and weakly amenable subalgebras. Among other consequences, we demonstrate the closedness of amenable subalgebras admitting state-preserving conditional expectations and analyze the semicontinuity and failure of continuity of the Cowling-Haagerup constant as a function on subalgebras. Finally, we investigate the space of von Neumann subalgebras that are not the image of state preserving conditional expectations for a fixed faithful normal state. For several important classes of von Neumann algebras, such as type ${\rm III}_\lambda$ factors with $0 \le \lambda < 1$ and type ${\rm III}_1$ factors with a state whose centraliser is infinite dimensional, we show that the subalgebras lacking state-preserving conditional expectations form an open and dense subset. Thus, in these settings, the generic subalgebra is not the range of state preserving conditional expectation.

math.OA↗

arXiv2026

Harnessing diamond surface features for dense and aligned NV ensembles

Controlling nitrogen doping in diamond is key to advancing nitrogen-vacancy (NV) center devices. We harness the hillock, a typically undesirable surface feature, to incorporate high densities of grown-in, aligned NV-centers on a (001)-oriented substrate. Enhanced cathodoluminescence at hillock sidewalls is correlated via nanoSIMS to up to 1000x greater nitrogen incorporation compared to the planar film. We find that these hillocks are associated with stacking faults and edge-type dislocations, consistent with an origin in surface preparation rather than substrate screw dislocations. Yet, the growth is orderly enough that each of the four hillock sidewalls hosts a distinct NV orientation. A 1.7-2% grown-in NV/substitutional nitrogen (P1) ratio, 4x higher than typical (001)-oriented growth, is measured via NV decoherence analysis. By revealing that spontaneously formed hillocks act as natural laboratories for dense, aligned NV formation, this work motivates systematic investigation of facet-dependent nitrogen incorporation and preferential NV alignment in (001) diamond.

cond-mat.mtrl-sci↗

arXiv2026

Shining light on short-range atomic ordering in semiconductors alloys

The functional properties of semiconductors are typically controlled by tailoring their chemical composition and their state of strain, and by controlling their long-range structural order, including the presence of extended defects such as dislocations. In addition to these approaches, theoretical predictions suggest that short-range order (SRO) of atoms in group-IV semiconductor alloys can modify the bandgap, a defining property of any semiconductor. Herein, a new machine learning enabled, computation-guided methodology for extended X-ray absorption fine structure (EXAFS) analysis of SRO is used to quantify the effects of local atomic order on the bandgap of germanium-tin (GeSn) alloy single crystal nanostructures with well-controlled strain and composition. Correlative analysis of EXAFS and photoluminescence (PL) establishes the relationship between bandgap and the Warren-Cowley short-range order (WC-SRO) parameter of the GeSn alloys. It is further demonstrated that SRO can be tuned over a broad range by post-deposition annealing of the alloy crystals. This work establishes control of SRO as an important design parameter for semiconducting properties and suggests the potential for quantitative measurement and tuning of SRO in other semiconductor alloy systems.

cond-mat.mtrl-sci↗

arXiv2026

MoltGraph: A Longitudinal Temporal Graph Dataset of Moltbook for Coordinated-Agent Detection

Agent-native social platforms such as Moltbook are rapidly emerging, yet they inherit and amplify classical influence and abuse attacks, where coordinated agents strategically comment and upvote to manipulate visibility and propagate narratives across communities. However, rigorous measurement and learning-based monitoring remain constrained by the absence of longitudinal, graph-native datasets for agentic social networks that jointly capture heterogeneous interactions, temporal drift, and visibility signals needed to connect coordination behavior to downstream exposure. We introduce MoltGraph as a realistic longitudinal agentic social-network graph dataset for studying how agents behave, coordinate, and evolve in the wild, enabling reproducible measurement on emerging multi-agent social ecosystems. Using MoltGraph, we provide the first graph-centric characterization of Moltbook as a dynamic network: (i) heavy-tailed connectivity with power-law exponents in the range alpha in [1.86, 2.72], (ii) accelerating hub formation and attention centralization where the top 1% agents account for 29.00% of engagements, (iii) bursty, short-lived coordination episodes, 98.33% last under 24 hours, and (iv) measurable exposure effects across submolts. In matched analyses, posts receiving coordinated engagement exhibit 506.35% higher early interaction rates (within H=5 days) and 242.63% higher downstream exposure in feeds than non-coordinated controls.

cs.SI↗

arXiv2026

Red-Teaming Claude Opus and ChatGPT-based Security Advisors for Trusted Execution Environments

Trusted Execution Environments (TEEs) (e.g., Intel SGX and ArmTrustZone) aim to protect sensitive computation from a compromised operating system, yet real deployments remain vulnerable to microarchitectural leakage, side-channel attacks, and fault injection. In parallel, security teams increasingly rely on Large Language Model (LLM) assistants as security advisors for TEE architecture review, mitigation planning, and vulnerability triage. This creates a socio-technical risk surface: assistants may hallucinate TEE mechanisms, overclaim guarantees (e.g., what attestation does and does not establish), or behave unsafely under adversarial prompting. We present a red-teaming study of two prevalently deployed LLM assistants in the role of TEE security advisors: ChatGPT-5.2 and Claude Opus-4.6, focusing on the inherent limitations and transferability of prompt-induced failures across LLMs. We introduce TEE-RedBench, a TEE-grounded evaluation methodology comprising (i) a TEE-specific threat model for LLM-mediated security work, (ii) a structured prompt suite spanning SGX and TrustZone architecture, attestation and key management, threat modeling, and non-operational mitigation guidance, along with policy-bound misuse probes, and (iii) an annotation rubric that jointly measures technical correctness, groundedness, uncertainty calibration, refusal quality, and safe helpfulness. We find that some failures are not purely idiosyncratic, transferring up to 12.02% across LLM assistants, and we connect these outcomes to secure architecture by outlining an "LLM-in-the-loop" evaluation pipeline: policy gating, retrieval grounding, structured templates, and lightweight verification checks that, when combined, reduce failures by 80.62%.

cs.CR↗

arXiv2026

Epitaxial Growth of Anisotropic SnSe on GaAs(001) via Step-Edge Orientation Control

Epitaxial growth of orthorhombic SnSe on cubic substrates is challenging due to lattice-symmetry mismatch and anisotropic bonding. Here we demonstrate that epitaxial films with sharp interfaces can be achieved for layered SnSe grown directly on on-axis and 4 degree miscut GaAs(001) substrates. The substrate miscut strongly influences the growth morphology, evolving from spirals on on-axis GaAs to a terraced structure on miscut GaAs. X-ray diffraction reveals that on-axis GaAs supports SnSe with two in-plane orientation variants, whereas the miscut substrate stabilizes a single orientation and introduces a small out-of-plane tilt. Accordingly, in-plane optical anisotropy is enhanced in the single variant film compared to the double variant, as determined by cross-polar reflectance. High-resolution TEM shows that the SnSe/GaAs interface is atomically abrupt and incoherent, characteristic of quasi-van der Waals epitaxy. We find a pronounced tendency for the zigzag edges of SnSe to align parallel to step edges on both substrates, and we show that step-skipping nucleation and layer growth on the miscut substrate leads to the additional tilt. These results establish direct SnSe/GaAs heteroepitaxy as a route to integrate anisotropic layered semiconductors with cubic platforms, and show that miscut substrates provide additional control over in-plane anisotropy.

cond-mat.mtrl-sci↗

arXiv2026

Optimal Transport-Guided Adversarial Attacks on Graph Neural Network-Based Bot Detection

The rise of bot accounts on social media poses significant risks to public discourse. To address this threat, modern bot detectors increasingly rely on Graph Neural Networks (GNNs). However, the effectiveness of these GNN-based detectors in real-world settings remains poorly understood. In practice, attackers continuously adapt their strategies as well as must operate under domain-specific and temporal constraints, which can fundamentally limit the applicability of existing attack methods. As a result, there is a critical need for robust GNN-based bot detection methods under realistic, constraint-aware attack scenarios. To address this gap, we introduce BOCLOAK to systematically evaluate the robustness of GNN-based social bot detection via both edge editing and node injection adversarial attacks under realistic constraints. BOCLOAK constructs a probability measure over spatio-temporal neighbor features and learns an optimal transport geometry that separates human and bot behaviors. It then decodes transport plans into sparse, plausible edge edits that evade detection while obeying real-world constraints. We evaluate BOCLOAK across three social bot datasets, five state-of-the-art bot detectors, three adversarial defenses, and compare it against four leading graph adversarial attack baselines. BOCLOAK achieves up to 80.13% higher attack success rates while using 99.80% less GPU memory under realistic real-world constraints. Most importantly, BOCLOAK shows that optimal transport provides a lightweight, principled framework for bridging the gap between adversarial attacks and real-world bot detection.

cs.LG↗

arXiv2025

Creation of Depth-Confined, Shallow Nitrogen-Vacancy Centers in Diamond With Tunable Density

Engineering shallow nitrogen-vacancy (NV) centers in diamond holds the key to unlocking new advances in nanoscale quantum sensing. We find that the creation of near-surface NVs through delta doping during diamond growth allows for tunable control over both NV depth confinement (with a twofold improvement relative to low-energy ion implantation) and NV density, ultimately resulting in highly-sensitive single defects and ensembles with coherence limited by NV-NV interactions. Additionally, we demonstrate the utility of our shallow delta-doped NVs by imaging magnetism in few-layer CrSBr, a two-dimensional magnet. We anticipate that the control afforded by near-surface delta doping will enable new developments in NV quantum sensing from nanoscale NMR to entanglement-enhanced metrology.

quant-ph↗

arXiv2025

GeoGuard: UWB Timing-Encoded Key Reconstruction for Location-Dependent, Geographically Bounded Decryption

Digital content distribution and propitiatory research driven industries face persistent risks from intellectual property theft and unauthorized redistribution. Conventional encryption schemes such as AES, TDES, ECC, and ElGamal provide strong cryptographic guarantees, but they remain fundamentally agnostic to where decryption takes place. In practice, this means that once a decryption key is leaked or intercepted, any adversary can misuse the key to decrypt the protected content from any location. This paper presents, GeoGuard, a location-dependent cryptosystem in which the decryption key is not transmitted as data but is implicitly encoded in the precise time-of-flight differences of ultra-wideband (UWB) data transmission packets. The system leverages precise timing hardware and a custom Timing-encoded Cryptographic Keying (TiCK) protocol to map a 32-byte SHA-256 AES key onto scheduled transmission timestamps. Only user located within an approved spatial location can observe the correct packet timing that aligns with the intended packet-reception timing pattern, enabling them to reconstruct the key. Eavesdroppers outside the authorized region observe an incorrect timing pattern, which yields incorrect keys. GeoGuard is designed to encrypt and transmit data, but decryption is only possible when the user is within the authorized area. Our evaluation demonstrates that the system (i) removes the need to share decryption passwords electronically or physically, (ii) ensures the decryption key cannot be recovered by the eavesdropper, and (iii) provides a non-trivial spatial tolerance for legitimate users

cs.CR↗

arXiv2025

LLM-driven Provenance Forensics for Threat Investigation and Detection

We introduce PROVSEEK, an LLM-powered agentic framework for automated provenance-driven forensic analysis and threat intelligence extraction. PROVSEEK employs specialized toolchains to dynamically retrieve relevant context by generating precise, context-aware queries that fuse knowledge from threat reports with evidence from system provenance data. The framework resolves provenance queries, orchestrates multiple role-specific agents, and synthesizes structured, ground-truth verifiable forensic summaries. By combining agent orchestration with Retrieval-Augmented Generation (RAG) and chain-of-thought (CoT) reasoning, data-guided filtration using a behavioral model, PROVSEEK enables adaptive multi-step analysis that iteratively refines hypotheses, verifies supporting evidence, and produces scalable, interpretable forensic explanations of attack behaviors. PROVSEEK is designed for automated threat investigation without task-specific training data, enabling forensic-style investigation even when no prior knowledge of the environment. We conduct a comprehensive evaluation on publicly available DARPA datasets, demonstrating that PROVSEEK outperforms retrieval-based methods for the intelligence extraction task, achieving a 34% improvement in contextual precision/recall; and for threat detection task, PROVSEEK achieves 22%/29% higher precision/recall compared to both a baseline agent approach and State-Of-The-Art (SOTA) Provenance-based Intrusion Detection System (PIDS). In our scalability study, we show that PROVSEEK increases token usage by 1.42x and latency by 1.63x as the database size increases 50x, making it optimal for large-scale deployment. We also conducted an ablation and error analysis study to show how different components of PROVSEEK affect the detection performance.

cs.CR↗

arXiv2025

Mid-infrared LEDs based on lattice-mismatched hybrid IV-VI/III-V heterojunctions

Light-emitting diodes (LEDs) can bridge the gap between narrow linewidth, expensive lasers and broadband, inefficient thermal globars for low-cost chemical sensing in the mid-infrared (mid-IR). However, the efficiency of III-V based mid-IR LEDs at room temperature is low, primarily limited by strong nonradiative Auger-Meitner recombination that is only partially overcome with complex quantum-engineered active regions. Here, we exploit the intrinsically low Auger Meitner recombination rates of the IV-VI semiconductors PbSe and PbSnSe, while leveraging the mature III-V platform through the fabrication of hybrid heterojunctions that mediate the ~8% lattice mismatch to GaAs. Electrically injected n-PbSe/p-GaAs LEDs emit at 3.8 um with output powers up to 400 uW under pulsed operation and a peak wall plug efficiency of 0.08% at room temperature, approaching the performance of commercial III-V LEDs at similar wavelengths. Incorporating 7% Sn extends the emission to 5 um in GeSe/PbSnSe/GaAs LEDs with output powers up to 45 uW. Notably, both devices operate despite threading dislocation densities on the order of 1e9/cm^2, underscoring the potential of hybrid IV-VI/III-V heterojunction architectures. We show that combining the complementary advantages of IV-VI and III-V semiconductors offers a simple and efficient mid IR optoelectronic platform for a rapidly expanding set of applications.

cond-mat.mtrl-sci↗

arXiv2025

PROVCREATOR: Synthesizing Complex Heterogenous Graphs with Node and Edge Attributes

The rise of graph-structured data has driven interest in graph learning and synthetic data generation. While successful in text and image domains, synthetic graph generation remains challenging -- especially for real-world graphs with complex, heterogeneous schemas. Existing research has focused mostly on homogeneous structures with simple attributes, limiting their usefulness and relevance for application domains requiring semantic fidelity. In this research, we introduce ProvCreator, a synthetic graph framework designed for complex heterogeneous graphs with high-dimensional node and edge attributes. ProvCreator formulates graph synthesis as a sequence generation task, enabling the use of transformer-based large language models. It features a versatile graph-to-sequence encoder-decoder that 1. losslessly encodes graph structure and attributes, 2. efficiently compresses large graphs for contextual modeling, and 3. supports end-to-end, learnable graph generation. To validate our research, we evaluate ProvCreator on two challenging domains: system provenance graphs in cybersecurity and knowledge graphs from IntelliGraph Benchmark Dataset. In both cases, ProvCreator captures intricate dependencies between structure and semantics, enabling the generation of realistic and privacy-aware synthetic datasets.

cs.LG↗

arXiv2025

Z-REx: Human-Interpretable GNN Explanations for Real Estate Recommendations

Transparency and interpretability are crucial for enhancing customer confidence and user engagement, especially when dealing with black-box Machine Learning (ML)-based recommendation systems. Modern recommendation systems leverage Graph Neural Network (GNN) due to their ability to produce high-quality recommendations in terms of both relevance and diversity. Therefore, the explainability of GNN is especially important for Link Prediction (LP) tasks since recommending relevant items can be viewed as predicting links between users and items. GNN explainability has been a well-studied field, but existing methods primarily focus on node or graph-level tasks, leaving a gap in LP explanation techniques. This work introduces Z-REx, a GNN explanation framework designed explicitly for heterogeneous link prediction tasks. Z-REx utilizes structural and attribute perturbation to identify critical substructures and important features while reducing the search space by leveraging domain-specific knowledge. In our experimentation, we show the efficacy of Z-REx in generating contextually relevant and human-interpretable explanations for ZiGNN, a GNN-based recommendation engine, using a real-world real-estate dataset from Zillow Group, Inc. We compare against State-of-The-Art (SOTA) GNN explainers to show Z-REx outperforms them by 61% in the Fidelity metric by producing superior human-interpretable explanations.

cs.IR↗

arXiv2025

Heteroepitaxial growth of highly anisotropic $Sb_{2}Se_{3}$ films on GaAs

The epitaxial integration of anisotropic materials with mainstream cubic semiconductors opens new routes to advanced electronic and photonic devices with directional properties. In this work, we synthesize heteroepitaxial thin films of orthorhombic "quasi-1D" $Sb_{2}Se_{3}$ on cubic GaAs(001) using molecular beam epitaxy. Traditionally, the synthesis of anisotropic films with low symmetry materials is challenging due to multiple grain orientations that form. On a macroscopic scale, such a film tends towards isotropic properties, even if individual grains possess anisotropic responses. We achieve epitaxial $Sb_{2}Se_{3}$ grains on pristine homoepitaxial GaAs templates at low temperatures of 180-200 {\deg}C. With the $Sb_{2}Se_{3}$ 1D axis aligned in-plane to GaAs [110] and the primary van der Waals direction lying out-of-plane, we find a birefringence of 0.2 between in-plane orthogonal directions and a giant out-of-plane birefringence greater than 1 at telecom wavelengths. Growth at higher temperatures up to 265 {\deg}C yields $Sb_{2}Se_{3}$ of an unusual in-plane rotated texture that further enhances the in-plane optical index anisotropy to 0.3.

cond-mat.mtrl-sci↗

arXiv2025

Scalable, nanoscale positioning of highly coherent color centers in prefabricated diamond nanostructures

Nanophotonic devices in color center-containing hosts provide efficient readout, control, and entanglement of the embedded emitters. Yet control over color center formation - in number, position, and coherence - in nanophotonic devices remains a challenge to scalability. Here, we report a controlled creation of highly coherent diamond nitrogen-vacancy (NV) centers with nanoscale three-dimensional localization in prefabricated nanostructures with high yield. Combining nitrogen $\delta$-doping during chemical vapor deposition diamond growth and localized electron irradiation, we form shallow NVs registered to the center of diamond nanopillars with wide tunability over NV number. We report positioning precision of ~ 4 nm in depth and 46(1) nm laterally in pillars (102(2) nm in bulk diamond). We reliably form single NV centers with long spin coherence times (average $T_2^{Hahn}$ = 98 $\mu s$) and 1.8x higher average photoluminescence compared to NV centers randomly positioned in pillars. We achieve a 3x improved yield of NV centers with single electron-spin sensitivity over conventional implantation-based methods. Our high-yield defect creation method will enable scalable production of solid-state defect sensors and processors.

quant-ph↗

arXiv2024

Epitaxial PbGeSe thin films and their photoluminescence in the mid-wave infrared

PbSe is a narrow bandgap IV-VI compound semiconductor with application in mid-wave infrared optoelectronics, thermoelectrics, and quantum devices. Alkaline earth or rare earth elements such as Sr and Eu can substitute Pb to widen the bandgap of PbSe in heterostructure devices, but they come with challenges such as deteriorating optical and electronic properties, even in dilute concentrations due to their dissimilar atomic nature. We substitute Pb instead with column-IV Ge and assess the potential of rocksalt phase PbGeSe as a wider bandgap semiconductor in thin films grown by molecular beam epitaxy on GaAs substrates. Low sticking of GeSe adatoms requires synthesis temperatures below 260 {\deg}C to incorporate Ge, but this yields poor structural and compositional uniformity as determined by X-ray diffraction. Consequently, as-grown films in the range Pb0.94Ge0.06Se to Pb0.83Ge0.17Se (6-17% Ge) show much less bandgap widening in photoluminescence than prior work on bulk crystals using absorption. We observe that post-growth rapid thermal annealing at temperatures of 375-450 {\deg}C improves the crystal quality and recovers bandgap widening. Rapid interdiffusion of Ge during annealing, however, remains a challenge in harnessing such PbGeSe materials for compositionally sharp heterostructures. Annealed 17%-Ge films emit light at 3-3.1 um with minimal shift in wavelength versus temperature. These samples are wider in bandgap than PbSe films by 55 meV at room temperature and the widening increases to 160 meV at 80 K, thanks to sharply different dependence of bandgap on temperature in PbSe and PbGeSe.

cond-mat.mtrl-sci↗