arXiv subjects

Manish Kumar

Publications and source records attributed to Manish Kumar.

At least 37 records · Page 2Linked to original sources

arXiv2026

Cotunneling theory and multiplet excitations: emergence of asymmetric line shape in inelastic scanning tunneling spectroscopy of correlated molecules on surfaces

Recent advances in on-surface chemistry, combined with scanning probe microscopy, have enabled the synthesis of correlated molecules on surfaces and the characterization of their chemical and electronic properties with unprecedented spatial resolution. Low-energy magnetic excitations of individual molecules are frequently investigated by scanning tunneling spectroscopy (STS) and often appear as symmetric step-like features in the differential conductance as a function of bias voltage. The interpretation of such steps is well established within cotunneling theory and effective model Hamiltonians (e.g., Hubbard- and spin-based models). Here, we extend the cotunneling formalism to general multireference systems. We show that multireference character, together with orbital-dependent and strongly asymmetric tip/substrate couplings, can produce pronounced asymmetric line shapes in inelastic STS. These results provide an alternative microscopic mechanism for the asymmetric peaks and dips near the Fermi level frequently observed in STS experiments.

cond-mat.mes-hall↗

arXiv2026

LLM-Augmented Changepoint Detection: A Framework for Ensemble Detection and Automated Explanation

This paper introduces a novel changepoint detection framework that combines ensemble statistical methods with Large Language Models (LLMs) to enhance both detection accuracy and the interpretability of regime changes in time series data. Two critical limitations in the field are addressed. First, individual detection methods exhibit complementary strengths and weaknesses depending on data characteristics, making method selection non-trivial and prone to suboptimal results. Second, automated, contextual explanations for detected changes are largely absent. The proposed ensemble method aggregates results from ten distinct changepoint detection algorithms, achieving superior performance and robustness compared to individual methods. Additionally, an LLM-powered explanation pipeline automatically generates contextual narratives, linking detected changepoints to potential real-world historical events. For private or domain-specific data, a Retrieval-Augmented Generation (RAG) solution enables explanations grounded in user-provided documents. The open source Python framework demonstrates practical utility in diverse domains, including finance, political science, and environmental science, transforming raw statistical output into actionable insights for analysts and decision-makers.

cs.CL↗

arXiv2026

Strongly entangled Quantum Spin Rings driven by Hückel rule

Quantum spin rings represent an intriguing platform for studying unconventional magnetic order and exotic quantum phases, and they are also promising materials for emerging quantum technologies. Conventional spin systems consist of a set of weakly interacting localized spins that are well described by the Heisenberg spin models. Here, we demonstrate that strong interactions between radical centers in macrocycles of different sizes lead to fluctuations in the total number of unpaired electrons and to non-trivial antiferromagnetic order that extends beyond the Heisenberg picture. We demonstrate that the electronic structure of these spin rings is governed by the concept of 4n/4n+2 Hückel (anti)aromaticity for even-membered rings, whereas odd-membered rings possess a highly degenerate frustrated magnetic ground state. The strongly coupled spin rings are experimentally realized through the on-surface synthesis of π-magnetic carbon-based macrocycles, which consist of [2]triangulene units. The close correlation between the electronic structure and the Hückel aromaticity rule is revealed by scanning tunneling spectroscopy and multireference calculations. This work establishes a novel design principle employing the concept of Hückel aromaticity for quantum spin macrocycles.

cond-mat.mes-hall↗

arXiv2026

Sublinear-Time Reconfiguration of Programmable Matter with Joint Movements

We study centralized reconfiguration problems for geometric amoebot structures. A set of $n$ amoebots occupy nodes on the triangular grid and can reconfigure via expansion and contraction operations. We focus on the joint movement extension, where amoebots may expand and contract in parallel, enabling coordinated motion of larger substructures. Prior work introduced this extension and analyzed reconfiguration under additional assumptions such as metamodules. In contrast, we investigate the intrinsic dynamics of reconfiguration without such assumptions by restricting attention to centralized algorithms, leaving distributed solutions for future work. We study the reconfiguration problem between two classes of amoebot structures $A$ and $B$: For every structure $S\in A$, the goal is to compute a schedule that reconfigures $S$ into some structure $S'\in B$. Our focus is on sublinear-time algorithms. We affirmatively answer the open problem by Padalkin et al. (Auton. Robots, 2025) whether a within-the-model sublinear-time universal reconfiguration algorithm is possible, by proving that any structure can be reconfigured into a canonical line-segment structure in $O(\sqrt{n}\log n)$ rounds. Additionally, we give a constant-time algorithm for reconfiguring any spiral structure into a line segment. These results are enabled by new constant-time primitives that facilitate efficient parallel movement. Our findings demonstrate that the joint movement model supports sublinear reconfiguration without auxiliary assumptions. A central open question is whether universal reconfiguration within this model can be achieved in polylogarithmic or even constant time.

cs.DS↗

arXiv2026

Knife-edge removal in schlieren imaging

A knife-edge is recognized as a critical component of the schlieren imaging system. This knife-edge serves as a cutoff element, which is required to achieve high sensitivity in the schlieren imaging setup. This article describes a method for totally removing the knife-edge from a normal schlieren imaging system while maintaining the same sensitivity. In this approach, the camera lens's internal aperture acts like the cutoff element making an external knife-edge redundant. This method simplifies the schlieren setup and also reduces the setup cost due to the reduced number of optical or optomechanical elements. We show flow visualization data that clearly demonstrate that schlieren contrast can be obtained by employing a lens's internal aperture as the cutoff.

physics.optics↗

arXiv2026

Equilibrium Thermochemistry and Crystallographic Morphology of Manganese Sulfide Nanocrystals

Manganese sulfide (MnS) is a p-type magnetic semiconductor whose physicochemical properties are sensitive to nanocrystal (NC) morphology, yet the thermodynamic driving forces governing morphology across MnS polymorphs remain poorly understood. Here, we use density functional theory (DFT) to predict the equilibrium morphologies of rock salt (RS), zinc blende (ZB), and wurtzite (WZ) MnS NCs as a function of the relative chemical potential of sulfur, $Δμ_{S}$. Benchmarking against Heyd$\unicode{x2013}$Scuseria$\unicode{x2013}$Ernzerhof (HSE06) hybrid functional calculations reveals that the r$^2$SCAN meta-generalized gradient approximation reproduces experimental lattice constants and thermochemical reaction energies but underestimates S-terminated polar surface energies by up to a factor of five; applying a Hubbard $U$ correction (r$^2$SCAN+$U$, $U = 2.7$ eV) to the Mn 3d states brings the results into close agreement with HSE06. Using the validated r$^2$SCAN+$U$ framework with the Gibbs$\unicode{x2013}$Wulff theorem, we predict that RS-MnS NCs favor nanocubes across nearly the entire stability window, ZB-MnS NCs transform from rhombic dodecahedra (Mn-rich) to polyhedra with 16 triangular faces (S-rich), and WZ-MnS NCs adopt rod-like morphologies with $Δμ_{S}$-sensitive base truncation. Synthesized RS-MnS NCs confirm the predicted cubic morphology, and high-temperature oxidative solution calorimetry yields an apparent surface energy of 1.15 $\pm$ 0.38 J$\cdot$m$^{-2}$, higher than the theoretical equilibrium value (0.42$\unicode{x2013}$0.43 J$\cdot$m$^{-2}$) due to high-index facet exposure, surface area uncertainty, and non-ideal surface configurations in real samples. This work establishes a framework for predicting the equilibrium morphologies of metal chalcogenide NCs.

cond-mat.mtrl-sci↗

arXiv2026

Water immersion single-mirror schlieren imaging system for flow visualization

Schlieren imaging is a popular optical technique for visualizing flow in transparent media. In-water high-sensitivity flow visualization, using schlieren imaging, is usually performed with a large-footprint two-mirror z-configuration. Here, we present a small footprint, easy-to-implement, single-mirror schlieren imaging system for in-water flow visualization. The same system is capable of high-sensitivity flow visualization in air as well. At its core, our system uses a concave mirror with water immersion. We present theoretical analysis and experimental results to show that this water immersion helps reduce the system's footprint by 25%. Our water immersion-based single-mirror schlieren imaging method additionally reduces mirror surface artifacts, increasing the sensitivity of flow visualization. This technique enables a low-cost schlieren system, as demonstrated experimentally using an inexpensive concave mirror. We also provide the experimental validation of high sensitivity in-water flow visualization for some transparent chemicals or solutions.

physics.flu-dyn↗

arXiv2026

Microwave Imaging of Edge Conductivity in Graphene at Charge Neutrality and Quantum Hall States

We report local conductivity imaging of edge states in monolayer graphene by millikelvin microwave impedance microscopy (MIM). At the charge-neutrality point, as the magnetic field increases, the local conductivity at the edge drops to zero more slowly than in the bulk. This behavior is consistent with the calculated spatial profile of the charge gap in the canted antiferromagnetic phase. For comparison, we also perform microwave imaging of integer quantum Hall states away from neutrality, which host dissipationless chiral edge channels. The evolution of the edge signal as a function of the bulk gap is fundamentally different between the Landau level filling factor $ν= 0$ and $|ν| \ge 1$ integer quantum Hall states, which can be qualitatively explained by numerical simulations and theoretical analysis. Our results provide a comprehensive microscopic picture of the edge and bulk states as the Fermi level moves across the unique Landau-level spectrum of graphene.

cond-mat.str-el↗

arXiv2026

Towards Human-AI Accessibility Mapping in India: VLM-Guided Annotations and POI-Centric Analysis in Chandigarh

Project Sidewalk is a web-based platform that enables crowdsourcing accessibility of sidewalks at city-scale by virtually walking through city streets using Google Street View. The tool has been used in 40 cities across the world, including the US, Mexico, Chile, and Europe. In this paper, we describe adaptation efforts to enable deployment in Chandigarh, India, including modifying annotation types, provided examples, and integrating VLM-based mission guidance, which adapts instructions based on a street scene and metadata analysis. Our evaluation with 3 annotators indicates the utility of AI-mission guidance with an average score of 4.66. Using this adapted Project Sidewalk tool, we conduct a Points of Interest (POI)-centric accessibility analysis for three sectors in Chandigarh with very different land uses, residential, commercial and institutional covering about 40 km of sidewalks. Across 40 km of roads audited in three sectors and around 230 POIs, we identified 1,644 of 2,913 locations where infrastructure improvements could enhance accessibility.

cs.HC↗

arXiv2026

Entanglement entropy as a probe of topological phase transitions

Entanglement entropy (EE) provides a powerful probe of quantum phases, yet its role in identifying topological phase transitions in disordered systems remains underexplored. We introduce an exact EE-based framework that captures topological phase transitions even in the presence of disorder. Specifically, for a class of Su-Schrieffer-Heeger (SSH) model variants, we show that the difference in EE between half-filled and near-half-filled ground states, $ΔS^{\mathcal{A}}$, vanishes in the topological phase but remains finite in the trivial phase, a direct consequence of edge-state localization. This behavior persists even in the presence of quasiperiodic or binary disorder. By analyzing domain-wall configurations in the SSH chain, we further show how subsystem tuning allows one to distinguish genuine topological zero-energy eigenstates from trivial localized states. Exact phase boundaries, derived from Lyapunov exponents via transfer matrices, agree closely with numerical results from $ΔS^{\mathcal{A}}$ and the topological invariant $\mathcal{Q}$, with instances where $ΔS^{\mathcal{A}}$ outperforms $\mathcal{Q}$. Our results highlight EE as a robust diagnostic tool and a potential bridge between quantum information and condensed matter approaches to topological matter.

cond-mat.str-el↗

arXiv2026

Quantum Spin-1/2 Rings Built from [2]Triangulene Molecular Units

Quantum spin rings represent fundamental model systems that exhibit distinctive quantum phenomena-such as quantum critical behavior and quasiparticle excitations-arising from their periodic boundary conditions and enhanced quantum fluctuations. Here, we report the on-surface synthesis and atomic-scale characterization of antiferromagnetic S=1/2 quantum spin rings composed of pristine and unmodified [2]triangulene units on a Au(111) surface. Using stepwise on-surface synthesis followed by STM tip-induced dehydrogenation, we precisely constructed cyclic five- and six-membered spin rings and investigated their spin states via scanning probe microscopy and multireference calculations. Nc-AFM imaging reveals that the six-membered ring retains a planar geometry, whereas the five-membered ring exhibits pronounced structural distortion. The six-membered ring hosts a uniform excitation gap that can be accurately described by a Heisenberg spin model and multireference CASCI calculations. In contrast, the distorted five-membered ring displays spin ground states with asymmetric spatial distributions due to degeneracy lifting induced by structural distortion. Our findings establish a versatile molecular platform for exploring correlated magnetism and quantum spin phenomena in cyclic organic magnetic architectures with disorder.

cond-mat.mtrl-sci↗

arXiv2026

Precision Oncology: Targeting Genomic Alterations and Cancer Signaling with Integrative Multi-Omics, Deep Learning and Network Biology in Medical Oncology

Cancer is a complex genetic disease involving uncontrolled cell growth and proliferation, and necessitates effective targeting of dysregulated cellular pathways underlying cancer progression. Multiple genetic and epigenetic alterations characterize tumor progression and define hallmarks of cancer. Importantly, patients with the same cancer type respond differently to available cancer treatments, likely due to tumor-specific DNA, RNA, and proteins, indicating the need for patient-specific treatment options. Precision oncology has evolved as a form of cancer therapy that is focused on genetic and molecular profiling of tumors to identify specific molecular alterations involved in carcinogenesis for tailored individualized cancer treatment. Advances in high-throughput sequencing technologies have enabled gene expression profiling, providing multiomics data for detailed molecular characterization of various tumors. Integration and analysis of various multiomic sequencing data are crucial in this regard, as they can reveal critical molecular changes, such as cancer-driving mutations, post-translational modifications, gene fusions, amplifications, and alterations in signaling networks within tumors. Furthermore, the role of computational techniques such as artificial intelligence and deep learning, in analyzing complex data and identifying patterns of disease development for better outcomes is now well established in precision medicine. Additionally, AI-powered multi-omics and network biology have been harnessed to integrate and analyze biological data through networks, which may prove crucial in solving key problems facing precision oncology. This article aims to briefly explain the foundations and frontiers of precision oncology in the context of cutting-edge developments in tools and techniques associated with it, and try to assess its scope and importance in achieving the intended goals.

q-bio.OT↗

arXiv2026

Adsorption-induced surface magnetism

We report the emergence of adsorption-induced magnetism from heterohelicene molecules on a non-magnetic Cu(100) surface. Spin-polarized low-energy electron microscopy (SP-LEEM) measurements reveal spin-dependent electron reflectivity for enantiopure 7,12,17-trioxa[11]helicene (TO[11]H) monolayers, indicating the formation of a spin-polarized state localized in the topmost copper layer. Control experiments on clean Cu(100) and TO[11]H on highly oriented pyrolytic graphite show no such effect, excluding artifacts and chirality-induced spin selectivity as origins. Spin-polarized density functional theory calculations with hybrid functionals attribute the magnetism to strong chemisorption, which induces hybridization between the molecular HOMO and copper s- and d-states, driving asymmetric spin-polarized charge redistribution at the interface. An extended Newns-Anderson-Grimley model incorporating on-site Coulomb repulsion in Cu d-orbitals reproduces the emergence of interfacial spin polarization above a threshold interaction strength, highlighting the key roles of hybridization parameters and Coulomb correlation. These findings reveal a mechanism for inducing magnetism at molecule-metal interfaces without inherently magnetic components, offering avenues for engineering spin-polarized states in organic-inorganic hybrid systems.

cond-mat.mtrl-sci↗

arXiv2026

Continuous Fairness On Data Streams

We study the problem of enforcing continuous group fairness over windows in data streams. We propose a novel fairness model that ensures group fairness at a finer granularity level (referred to as block) within each sliding window. This formulation is particularly useful when the window size is large, making it desirable to enforce fairness at a finer granularity. Within this framework, we address two key challenges: efficiently monitoring whether each sliding window satisfies block-level group fairness, and reordering the current window as effectively as possible when fairness is violated. To enable real-time monitoring, we design sketch-based data structures that maintain attribute distributions with minimal overhead. We also develop optimal, efficient algorithms for the reordering task, supported by rigorous theoretical guarantees. Our evaluation on four real-world streaming scenarios demonstrates the practical effectiveness of our approach. We achieve millisecond-level processing and a throughput of approximately 30,000 queries per second on average, depending on system parameters. The stream reordering algorithm improves block-level group fairness by up to 95% in certain cases, and by 50-60% on average across datasets. A qualitative study further highlights the advantages of block-level fairness compared to window-level fairness.

cs.LG↗

arXiv2026

Pervasive spin-triplet superconductivity in rhombohedral graphene

Magnetic fields typically suppress superconductivity once the Zeeman energy exceeds the pairing gap, unless mechanisms such as unconventional pairing, strong spin-orbit coupling, or intrinsic magnetism intervene. Several graphene platforms realize such mitigating routes, exhibiting superconductivity resilient to magnetic fields. Here we report superconductivity in rhombohedral heptalayer graphene that is both induced and stabilized by in-plane magnetic field ($B_{\parallel}$), with critical fields far beyond the Pauli paramagnetic limit. The superconductivity spans a wide gate range and emerges from a sharp zero-field resistive ridge that tracks approximately constant conduction band filling. The presence of zero-field superconductivity and the evolution of the critical temperature with $B_{\parallel}$ are highly gate sensitive. We also observe a weak superconducting diode effect in several distinct regimes within the superconducting phase, including nearby to an integer quantum anomalous Hall state generated by a boron nitride moiré superlattice, indicating a potential coexistence of valley imbalance and superconductivity. These results establish several intriguing new properties of spin-triplet, field-induced superconductivity in a thick rhombohedral graphene stack.

cond-mat.mes-hall↗

arXiv2025

Role of Wadsley Defects and Cation Disorder to Enhance MoNb12O33 Diffusion

Wadsley-Roth (WR) niobates have emerged as high-rate anode materials that can combine rapid ionic diffusion with good electronic conductivity. WR compounds have been defect-enhanced by limited annealing, however, such materials often contain multiple types of defects. In particular, both Wadsley defects (variable block size) and transition metal disorder have the potential to modify transport rates, however the corresponding effects are not well understood mechanistically. Here, MoNb12O33 (MNO) was calcined at two different temperatures to compare a defect-rich condition (MNO-800) with a proximal order-rich condition (MNO-900) as assessed through XRD, XANES, EXAFS, and STEM characterizations. Galvanostatically cycled lithium half cells of MNO-800 exhibited additional capacity (307 mAh/g at 0.1C, 4.66% higher) and improved high-rate capacity of 200 mAhg-1 at 10C. ICI-based overpotential analysis identified solid state diffusion as the dominant rate limiting process where MNO-800 correspondingly exhibited ~3X faster capacity-weighted diffusivity. A machine-learning interatomic potential was trained to density functional theory and then applied with molecular dynamics (MLIP-MD) to examine the possible roles of Wadsley defects and transition metal disorder. For both defect-types, Li was found to populate and activate fast diffusion paths from window sites at lower extents of lithiation as compared to the order-rich model.

cond-mat.mtrl-sci↗

arXiv2025

Controversy and consensus: common ground and best practices for life cycle assessment of emerging technologies

Public and private interest in life cycle assessment (LCA) has grown as environmental disclosure norms tighten, driving demand for decision-relevant assessment early in technological development cycles. Early-stage LCA has the potential to guide design choices, steer innovation, and mitigate lock-in of adverse environmental impacts. However, many aspects of early-stage LCA practice remain unsettled. We convened experts in a series of Faraday Discussion-style workshops to address recurring debates across six key topics for emerging technologies: appropriate use of LCA, uncertainty, comparison with incumbents, standardization, scale-up, and stakeholder engagement. For each issue, we present a declarative resolution, summarize key arguments for and against it, identify points of consensus, and provide recommendations. Across topics, the research network converged on practical priorities including framing studies to the decision context; setting minimum reporting expectations for data and study quality; and explicitly stating limits of transferability for scenario-based uncertainty assessment or analytically scaled-up projections. Disagreements persisted on when to formalize standards and how extensively uncertainty can/should be treated for low-maturity technologies. Supplementing the workshop findings with examples and context from relevant literature, we synthesize outcomes into a set of shared challenges and research priorities to strengthen transparent, evidence-based, and context-informed approaches for early-stage LCA.

cs.CY↗

arXiv2025

An Expert-grounded benchmark of General Purpose LLMs in LCA

Purpose: Artificial intelligence (AI), and in particular large language models (LLMs), are increasingly being explored as tools to support life cycle assessment (LCA). While demonstrations exist across environmental and social domains, systematic evidence on their reliability, robustness, and usability remains limited. This study provides the first expert-grounded benchmark of LLMs in LCA, addressing the absence of standardized evaluation frameworks in a field where no clear ground truth or consensus protocols exist. Methods: We evaluated eleven general-purpose LLMs, spanning both commercial and open-source families, across 22 LCA-related tasks. Seventeen experienced practitioners reviewed model outputs against criteria directly relevant to LCA practice, including scientific accuracy, explanation quality, robustness, verifiability, and adherence to instructions. We collected 168 expert reviews. Results: Experts judged 37% of responses to contain inaccurate or misleading information. Ratings of accuracy and quality of explanation were generally rated average or good on many models even smaller models, and format adherence was generally rated favourably. Hallucination rates varied significantly, with some models producing hallucinated citations at rates of up to 40%. There was no clear-cut distinction between ratings on open-weight versus closed-weight LLMs, with open-weight models outperforming or competing on par with closed-weight models on criteria such as accuracy and quality of explanation. Conclusion: These findings highlight the risks of applying LLMs naïvely in LCA, such as when LLMs are treated as free-form oracles, while also showing benefits especially around quality of explanation and alleviating labour intensiveness of simple tasks. The use of general-purpose LLMs without grounding mechanisms presents ...

cs.CL↗