arXiv subjects

Manish Kumar

Publications and source records attributed to Manish Kumar.

At least 73 records · Page 4Linked to original sources

arXiv2025

Computational screening and experimental validation of promising Wadsley-Roth Niobates

The growing demand for efficient, high-capacity energy storage systems has driven extensive research into advanced materials for lithium-ion batteries. Among the various candidates, Wadsley-Roth (WR) niobates have emerged as a promising class of materials for fast Li+ storage due to rapid ion diffusion within their ReO3-like blocks in combination with good electronic conductivity along the shear planes. Despite the remarkable features of WR phases, there are presently less than 30 known structures which limits identification of structure-property relationships for improved performance as well as the identification of phases with more earth-abundant elements. In this work, we have dramatically expanded the set of potentially (meta)stable compositions (with $Δ$ Hd < 22 meV/atom) to 1301 (out of 3283) through high-throughput screening with density functional theory (DFT). This large space of compound was generated through single- and double-site substitution into 10 known WR-niobate prototypes using 48 elements across the periodic table. To confirm the structure predictions, we successfully synthesized and validated with X-ray diffraction a new material, MoWNb24O66. The measured lithium diffusivity in MoWNb24O66 has a peak value of 1.0x10-16 m2/s at 1.45 V vs. Li/Li+ and achieved 225 mAh/g at 5C. Thus a computationally predicted phase was realized experimentally with performance exceeding Nb16W5O55, a recent WR benchmark. Overall, the computational dataset of potentially stable novel compounds and with one realized that has competitive performance provide a valuable guide for experimentalists in discovering new durable battery materials.

cond-mat.mtrl-sci↗

arXiv2025

On-surface Synthesis of a Ferromagnetic Molecular Spin Trimer

Triangulenes are prototypical examples of open-shell nanographenes. Their magnetic properties, arising from the presence of unpaired $π$ electrons, can be extensively tuned by modifying their size and shape or by introducing heteroatoms. Different triangulene derivatives have been designed and synthesized in recent years, thanks to the development of on-surface synthesis strategies. Triangulene-based nanostructures with polyradical character, hosting several interacting spin units, can be challenging to fabricate but are particularly interesting for potential applications in carbon-based spintronics. Here, we combine pristine and N-doped triangulenes into a more complex nanographene, \textbf{TTAT}, predicted to possess three unpaired $π$ electrons delocalized along the zigzag periphery. We generate the molecule on an Au(111) surface and detect direct fingerprints of multi-radical coupling and high-spin state using scanning tunneling microscopy and spectroscopy. With the support of theoretical calculations, we show that its three radical units are localized at distinct parts of the molecule and couple via symmetric ferromagnetic interactions, which result in a $S=3/2$ ground state, thus demonstrating the realization of a molecular ferromagnetic Heisenberg-like spin trimer

cond-mat.mes-hall↗

arXiv2025

Hybrid Fingerprint-based Positioning in Cell-Free Massive MIMO Systems

Recently, there has been an increasing interest in 6G technology for integrated sensing and communications, where positioning stands out as a key application. In the realm of 6G, cell-free massive multiple-input multiple-output (MIMO) systems, featuring distributed base stations equipped with a large number of antennas, present an abundant source of angle-of-arrival (AOA) information that could be exploited for positioning applications. In this paper we leverage this AOA information at the base stations using the multiple signal classification (MUSIC) algorithm, in conjunction with received signal strength (RSS) for positioning through Gaussian process regression (GPR). An AOA fingerprint database is constructed by capturing the angle data from multiple locations across the network area and is combined with RSS data from the same locations to form a hybrid fingerprint which is then used to train a GPR model employing a squared exponential kernel. The trained regression model is subsequently utilized to estimate the location of a user equipment. Simulations demonstrate that the GPR model with hybrid input achieves better positioning accuracy than traditional GPR models utilizing RSS-only and AOA-only inputs.

eess.SP↗

arXiv2025

Magnetic ground state discrimination of a Polyradical Nanog-raphene using Nickelocene-Functionalized Tips

Molecular magnets are a promising class of materials with exciting properties and applications. However, a profound understanding and application of such materials depends on the accurate detection of their electronic and magnetic properties. Despite the availability of experimental techniques that can sense the magnetic signal, the exact determination of the spin ground states and spatial distribution of exchange interaction of strongly correlated single-molecule magnets remains challenging. Here, we demonstrate that scanning probe microscopy with a nickelocene-functionalized probe can distinguish between nearly degenerate multireference ground states of single-molecule π-magnets and map their spatial distribution of the exchange interaction. This method expands the already outstanding imaging capabilities of scanning probe microscopy for characterizing the chemical and electronic structures of individual molecules, paving the way for the study of strongly correlated molecular magnets with unprecedented spatial resolution.

cond-mat.mtrl-sci↗

arXiv2025

Theory of scanning tunneling spectroscopy beyond one-electron molecular orbitals: can we image molecular orbitals?

The interpretation of experimental spatially resolved scanning tunneling spectroscopy (STS) maps of close-shell molecules on surfaces is usually interpreted within the framework of oneelectron molecular orbitals. Although this standard practice often gives relatively good agreement with experimental data, it contradicts one of the basic assumptions of quantum mechanics, postulating the impossibility of direct observation of the wavefunction, i.e., individual molecular orbitals. The scanning probe community often considers this contradiction about observing molecular orbitals as a philosophical question rather than a genuine problem. Moreover, in the case of polyradical strongly correlated molecules, the interpretation of STS maps based on one-electron molecular orbitals often fails. Thus, for a precise interpretation of STS maps and their connection to the electronic structure of molecules, a theoretical description, including non-equilibrium tunneling processes going beyond one-electron process, is required. In this contribution, we first show why, in selected cases, it is possible to achieve good agreement with experimental data based on one-electron canonical molecular orbitals and Tersoff-Hamann approximation. Next, we will show that for an accurate interpretation of strongly correlated molecules, it is necessary to describe the process of removing/adding an electron within the formalism of many-electron wavefunctions for the neutral and charged states. This can be accomplished by the concept of so-called Dyson orbitals.

cond-mat.mtrl-sci↗

arXiv2025

Mambular: A Sequential Model for Tabular Deep Learning

The analysis of tabular data has traditionally been dominated by gradient-boosted decision trees (GBDTs), known for their proficiency with mixed categorical and numerical features. However, recent deep learning innovations are challenging this dominance. This paper investigates the use of autoregressive state-space models for tabular data and compares their performance against established benchmark models. Additionally, we explore various adaptations of these models, including different pooling strategies, feature interaction mechanisms, and bi-directional processing techniques to understand their effectiveness for tabular data. Our findings indicate that interpreting features as a sequence and processing them and their interactions through structured state-space layers can lead to significant performance improvement. This research underscores the versatility of autoregressive models in tabular data analysis, positioning them as a promising alternative that could substantially enhance deep learning capabilities in this traditionally challenging area. The source code is available at https://github.com/basf/mamba-tabular.

cs.LG↗

arXiv2025

Dispersion is (Almost) Optimal under (A)synchrony

The dispersion problem has received much attention recently in the distributed computing literature. In this problem, $k\leq n$ agents placed initially arbitrarily on the nodes of an $n$-node, $m$-edge anonymous graph of maximum degree $Δ$ have to reposition autonomously to reach a configuration in which each agent is on a distinct node of the graph. Dispersion is interesting as well as important due to its connections to many fundamental coordination problems by mobile agents on graphs, such as exploration, scattering, load balancing, relocation of self-driven electric cars (robots) to recharge stations (nodes), etc. The objective has been to provide a solution that optimizes simultaneously time and memory complexities. There exist graphs for which the lower bound on time complexity is $Ω(k)$. Memory complexity is $Ω(\log k)$ per agent independent of graph topology. The state-of-the-art algorithms have (i) time complexity $O(k\log^2k)$ and memory complexity $O(\log(k+Δ))$ under the synchronous setting [DISC'24] and (ii) time complexity $O(\min\{m,kΔ\})$ and memory complexity $O(\log(k+Δ))$ under the asynchronous setting [OPODIS'21]. In this paper, we improve substantially on this state-of-the-art. Under the synchronous setting as in [DISC'24], we present the first optimal $O(k)$ time algorithm keeping memory complexity $O(\log (k+Δ))$. Under the asynchronous setting as in [OPODIS'21], we present the first algorithm with time complexity $O(k\log k)$ keeping memory complexity $O(\log (k+Δ))$, which is time-optimal within an $O(\log k)$ factor despite asynchrony. Both results were obtained through novel techniques to quickly find empty nodes to settle agents, which may be of independent interest.

cs.DC↗

arXiv2025

Reconfiguration and Locomotion with Joint Movements in the Amoebot Model

We are considering the geometric amoebot model where a set of $n$ amoebots is placed on the triangular grid. An amoebot is able to send information to its neighbors, and to move via expansions and contractions. Since amoebots and information can only travel node by node, most problems have a natural lower bound of $Ω(D)$ where $D$ denotes the diameter of the structure. Inspired by the nervous and muscular system, Feldmann et al. have proposed the reconfigurable circuit extension and the joint movement extension of the amoebot model with the goal of breaking this lower bound. In the joint movement extension, the way amoebots move is altered. Amoebots become able to push and pull other amoebots. Feldmann et al. demonstrated the power of joint movements by transforming a line of amoebots into a rhombus within $O(\log n)$ rounds. However, they left the details of the extension open. The goal of this paper is therefore to formalize and extend the joint movement extension. In order to provide a proof of concept for the extension, we consider two fundamental problems of modular robot systems: reconfiguration and locomotion. We approach these problems by defining meta-modules of rhombical and hexagonal shape, respectively. The meta-modules are capable of movement primitives like sliding, rotating, and tunneling. This allows us to simulate reconfiguration algorithms of various modular robot systems. Finally, we construct three amoebot structures capable of locomotion by rolling, crawling, and walking, respectively.

cs.RO↗

arXiv2025

Direct Numerical Simulations of Droplet Impact onto Heated Surfaces using the Program Free Surface 3D (FS3D)

Droplet impact onto heated surfaces is a widespread process in industrial applications, particularly in the context of spray cooling techniques. Therefore, it is essential to study the complex phenomenon of droplet spreading, heat removal from a hot surface, and flow distribution during the impact. This study focuses on Direct Numerical Simulation (DNS) of the initial stage of a water droplet impact onto a highly conducting heated surface, below the saturation temperature of the liquid. The maximum spreading diameters at different impact velocities in the presence of a heated surface, are analysed. Free Surface 3D (FS3D), an in-house code developed at the Institute of Aerospace Thermodynamics, University of Stuttgart, is used for this work. A grid independence study investigates the resolution required to resolve the flow field around the droplet. As evaporation effects during the initial stage of the droplet impact process are negligible, they are ignored. However, for longer simulation times, evaporation plays a significant role in the process. Preparing for such simulations, an evaporating droplet in cross flow is simulated to study the performance gain in the newly implemented hybrid OpenMP and MPI parallelisation and red-black optimization in the evaporation routines of FS3D. Both the scaling limit and efficiency were improved by using the hybrid (MPI with OpenMP) parallelisation, while the red-black scheme optimization raised the efficiency only. An improved performance of 23% of the new version is achieved for a test case investigated with the tool MAQAO. Additionally, strong and weak scaling performance tests are conducted. The new version is found to scale up to 256 nodes compared to 128 nodes for the original version. The maximum time-cycles per hour (CPH) achieved with the new version is 35% higher compared to the previous version.

physics.flu-dyn↗

arXiv2025

Elastoinertial turbulence: Data-driven reduced-order model based on manifold dynamics

Elastoinertial turbulence (EIT) is a chaotic state that emerges in the flows of dilute polymer solutions. Direct numerical simulation (DNS) of EIT is highly computationally expensive due to the need to resolve the multi-scale nature of the system. While DNS of 2D EIT typically requires $O(10^6)$ degrees of freedom, we demonstrate here that a data-driven modeling framework allows for the construction of an accurate model with 50 degrees of freedom. We achieve a low-dimensional representation of the full state by first applying a viscoelastic variant of proper orthogonal decomposition to DNS results, and then using an autoencoder. The dynamics of this low-dimensional representation are learned using the neural ODE method, which approximates the vector field for the reduced dynamics as a neural network. The resulting low-dimensional data-driven model effectively captures short-time dynamics over the span of one correlation time, as well as long-time dynamics, particularly the self-similar, nested traveling wave structure of 2D EIT in the parameter range considered.

physics.flu-dyn↗

arXiv2024

Distributed Download from an External Data Source in Faulty Majority Settings

We extend the study of retrieval problems in distributed networks, focusing on improving the efficiency and resilience of protocols in the \emph{Data Retrieval (DR) Model}. The DR Model consists of a complete network (i.e., a clique) with $k$ peers, up to $βk$ of which may be Byzantine (for $β\in [0, 1)$), and a trusted \emph{External Data Source} comprising an array $X$ of $n$ bits ($n \gg k$) that the peers can query. Additionally, the peers can also send messages to each other. In this work, we focus on the Download problem that requires all peers to learn $X$. Our primary goal is to minimize the maximum number of queries made by any honest peer and additionally optimize time. We begin with a randomized algorithm for the Download problem that achieves optimal query complexity up to a logarithmic factor. For the stronger dynamic adversary that can change the set of Byzantine peers from one round to the next, we achieve the optimal time complexity in peer-to-peer communication but with larger messages. In broadcast communication where all peers (including Byzantine peers) are required to send the same message to all peers, with larger messages, we achieve almost optimal time and query complexities for a dynamic adversary. Finally, in a more relaxed crash fault model, where peers stop responding after crashing, we address the Download problem in both synchronous and asynchronous settings. Using a deterministic protocol, we obtain nearly optimal results for both query complexity and message sizes in these scenarios.

cs.DC↗

arXiv2024

Robot Swarming over the internet

This paper considers cooperative control of robots involving two different testbed systems in remote locations with communication on the internet. This provides us the capability to exchange robots status like positions, velocities and directions needed for the swarming algorithm. The results show that all robots properly follow some leader defined one of the testbeds. Measurement of data exchange rates show no loss of packets, and average transfer delays stay within tolerance limits for practical applications. In our knowledge, the novelty of this paper concerns this kind of control over a large network like internet.

cs.RO↗

arXiv2024

Optimal Fault-Tolerant Dispersion on Oriented Grids

Dispersion of mobile robots over the nodes of an anonymous graph is an important problem and turns out to be a crucial subroutine for designing efficient algorithms for many fundamental graph problems via mobile robots. In this problem, starting from an arbitrary initial distribution of $n$ robots across the $n$ nodes, the goal is to achieve a final configuration where each node holds at most one robot. This paper investigates the dispersion problem on an oriented grid, considering the possibility of robot failures (crashes) at any time during the algorithm's execution. We present a crash-tolerant dispersion algorithm that solves the dispersion problem on an anonymous oriented grid in $O(\sqrt{n})$ time and using $O(\log n)$ bits of memory per robot. The algorithm is optimal in terms of both time and memory per robot. We further extend this algorithm to deal with weak Byzantine robots. The weak Byzantine fault dispersion algorithm takes optimal $O(\sqrt{n})$ rounds but requires $O(n\log n)$ bits of memory per robot.

cs.DC↗

arXiv2024

Theoretical model for multi-orbital Kondo screening in strongly correlated molecules with several unpaired electrons

The mechanism of Kondo screening in strongly correlated molecules with several unpaired electrons on a metal surface is still under debate. Here, we provide a theoretical framework that rationalizes the emergence of Kondo screening involving several extended molecular orbitals with unpaired electrons. We introduce a perturbative model, which provides simple rules to identify the presence of antiferromagnetic spin-flip channels involving charged molecular multiplets responsible for Kondo screening. The Kondo regime is confirmed by numerical renormalization group calculations. In addition, we introduce the concept of Kondo orbitals as molecular orbitals associated with the Kondo screening process, which provide a direct interpretation of experimental $dI/dV$ maps of Kondo resonances. We demonstrate that this theoretical framework can be applied to different strongly correlated open-shell molecules on metal surfaces, obtaining good agreement with previously published experimental data.

cond-mat.str-el↗

arXiv2024

Direct image of structure sheaf and parabolic stability

Let $f : X \rightarrow Y$ be a dominant generically smooth morphism between irreducible smooth projective curves over an algebraically closed field $k$ such that ${\rm Char}(k)> \text{degree}(f)$ if the characteristic of $k$ is nonzero. We prove that $(f_*{\mathcal O}_X)/{\mathcal O}_Y$ equipped with a natural parabolic structure is parabolic polystable. Several conditions are given that ensure that the parabolic vector bundle $(f_*{\mathcal O}_X)/{\mathcal O}_Y$ is actually parabolic stable.

math.AG↗

arXiv2024

Constructing Multiresolution Analysis via Wavelet Packets on Sobolev Space in Local Fields

We define Sobolev spaces $H^{\mathfrak{s}}(K_q)$ over a local field $K_q$ of finite characteristic $p>0$, where $q=p^c$ for a prime $p$ and $c\in \mathbb{N}$. This paper introduces novel fractal functions, such as the Weierstrass type and 3-adic Cantor type, as intriguing examples within these spaces and a few others. Employing prime elements, we develop a Multi-Resolution Analysis (MRA) and examine wavelet expansions, focusing on the orthogonality of both basic and fractal wavelet packets at various scales. We utilize convolution theory to construct Haar wavelet packets and demonstrate the orthogonality of all discussed wavelet packets within $H^{\mathfrak{s}}(K_q)$, enhancing the analytical capabilities of these Sobolev spaces.

math.RA↗

arXiv2024

Fullness of $q$-Araki-Woods factors

The $q$-Araki-Woods factor associated to a group of orthogonal transformations on a real separable Hilbert space $\mathsf{H}_{\mathbb{R}}$ is full as soon as $\dim\mathsf{H}_{\mathbb{R}}\geq 2$.

math.OA↗

arXiv2024

Nested traveling wave structures in elastoinertial turbulence

Elastoinertial turbulence (EIT) is a chaotic flow resulting from the interplay between inertia and viscoelasticity in wall bounded shear flows. Understanding EIT is important because it is thought to set a limit on the effectiveness of turbulent drag reduction in polymer solutions. Here, we analyze simulations of two dimensional EIT in channel flow using Spectral Proper Orthogonal Decomposition (SPOD), discovering a family of traveling wave structures that capture the sheetlike stress fluctuations that characterize EIT. The frequency dependence of the leading SPOD mode contains distinct peaks and the mode structures corresponding to these peaks exhibit well defined traveling structures. The structure of the dominant traveling mode exhibits shift reflect symmetry similar to the viscoelasticity modified Tollmien Schlichting (TS) wave, where the velocity fluctuation in the traveling mode is characterized by large scale regular structures spanning the channel and the polymer stress field is characterized by thin, inclined sheets of high polymer stress localized at the critical layers near the channel walls. The traveling structures corresponding to the higher frequency modes have a very similar structure, but are nested in a region roughly bounded by the critical layer positions of the next lower frequency mode. A simple theory based on the idea that the critical layers of mode $κ$ form the "walls" for the structure of mode $κ+1$ yields quantitative agreement with the observed wave speeds and critical layer positions, indicating self similarity between the structures. The physical idea behind this theory is that the sheetlike localized stress fluctuations in the critical layer prevent velocity fluctuations from penetrating them.

physics.flu-dyn↗