arXiv subjects

Mikhail Titov

Publications and source records attributed to Mikhail Titov.

At least 19 recordsLinked to original sources

arXiv2026

Boundary Kerr Signatures of the Interband-Coherence Hall Effect

We identify a Hall response carried by optically induced interband coherence rather than by a non-equilibrium band population. In a weakly doped zinc-blende semiconductor, a longitudinal dc field drives a transverse flux of the conduction--valence coherence created by near-gap light. Angular averaging eliminates the homogeneous coherence density, while a lateral boundary converts the transverse flux into an antisymmetric, edge-localized, helicity-odd polarization. The resulting Kerr signal requires neither spin-orbit coupling nor a spin, valley, or orbital accumulation within an individual band. Within the eight-band Kane model, we derive the boundary kinetic equation and obtain a complex propagation length controlled by optical detuning and interband dephasing. The edge profile is monotonic at optical resonance and develops damped spatial oscillations away from it. The response is enhanced by electron--hole asymmetry and by strong interband mixing, making narrow-gap semiconductors especially favorable to observe the effect. These results establish dc-driven Kerr microscopy as a direct probe of an interband-coherence Hall effect and of multiband quantum kinetics in real space.

cond-mat.dis-nn↗

arXiv2026

Magnetothermopower and particle-hole symmetry in a cuprate strange metal

Here, we report magnetothermopower measurements on overdoped (Bi,Pb)2(Sr,La)2CuO6+delta (Bi2201) single crystals in magnetic fields up to 35 T. Whereas the temperature dependence of the zero-field Seebeck coefficient S(T) can be captured using Boltzmann transport theory, the field-dependent response S(H) cannot. Instead, the magnetothermopower contains a large additional contribution whose field and temperature dependence is consistent with the presence of short-range superconducting order well above Tc. Combined with earlier Hall and magnetoresistance results, these data imply that the overdoped cuprate strange metal contains two transport sectors with distinct particle-hole symmetry: a conventional particle-hole-asymmetric Fermi-liquid (FL) contribution governing the Hall effect and the zero-field thermopower, and a nearly particle-hole-symmetric sector dominating the anomalous longitudinal magnetotransport. We formulate a phenomenological real-space model in which disconnected FL islands are embedded in a compensated Dirac liquid of phase-incoherent d-wave Bogoliubov quasiparticles. This picture reconciles conventional zero-field transport with anomalous magnetothermopower and magnetoresistance and offers a concrete framework for thinking about strange metallicity in overdoped cuprates.

cond-mat.supr-con↗

arXiv2026

Spin-orbit coupling by design in quantum state engineering of atomically defined quantum dots

Tuning spin-orbit coupling is essential in controlling both spin and charge in confined semiconductor nanostructures, yet it is rarely a truly controllable parameter. Here, we show control over the spin-orbit Hamiltonian in quantum dots and the resulting quantum states by tailoring the confinement potential with atomic-scale precision. Using scanning tunnelling microscopy and spectroscopy, we pattern individual Cs ions into designer quantum dot structures on the surface of indium antimonide, in which electrons from a two-dimensional electron gas are confined with chosen in-plane electric-field gradients. We then quantify the atomic level structure, both spatially resolving the orbital character of the electronic states and their magnetic-field evolution. We demonstrate that the level structure, including the induced zero-field splitting, can be tailored by the designed geometry of the local electric fields. These effects can be described using a Hamiltonian that allows consistent treatment of the confinement-induced spin-orbit coupling beyond the conventional Bychkov-Rashba description. This Hamiltonian is derived from a multiband k.p model and takes the energy dependence of the relevant physical parameters into account. Such precise control of spin-orbit coupling in semiconductor quantum dots is relevant to quantum and spintronic technologies.

cond-mat.mes-hall↗

arXiv2025

RHAPSODY: Execution of Hybrid AI-HPC Workflows at Scale

Hybrid AI-HPC workflows combine large-scale simulation, training, high-throughput inference, and tightly coupled, agent-driven control within a single execution campaign. These workflows impose heterogeneous and often conflicting requirements on runtime systems, spanning MPI executables, persistent AI services, fine-grained tasks, and low-latency AI-HPC coupling. Existing systems typically address only subsets of these requirements, limiting their ability to support emerging AI-HPC applications at scale. We present RHAPSODY, a multi-runtime middleware that enables concurrent execution of heterogeneous AI-HPC workloads through uniform abstractions for tasks, services, resources, and execution policies. Rather than replacing existing runtimes, RHAPSODY composes and coordinates them, allowing simulation codes, inference services, and agentic workflows to coexist within a single job allocation on leadership-class HPC platforms. We evaluate RHAPSODY with Dragon and vLLM on multiple HPC systems using representative heterogeneous, inference-at-scale, and tightly coupled AI-HPC workflows. Our results show that RHAPSODY introduces minimal runtime overhead, sustains increasing heterogeneity at scale, achieves near-linear scaling for high-throughput inference workloads, and data- and control-efficient coupling between AI and HPC tasks in agentic workflows.

cs.DC↗

arXiv2025

Disorder-assisted Spin-Filtering at Metal/Ferromagnet Interfaces: An Alternative Route to Anisotropic Magnetoresistance

We introduce a minimal interface-scattering mechanism that produces a sizable anisotropic magnetoresistance (AMR) in metal/ferromagnet bilayers (e.g., Pt/YIG) without invoking bulk spin or orbital Hall currents. In a $δ$-layer model with interfacial exchange and Rashba spin-orbit coupling, charge transfer at a high-quality interface creates a spin-selective phase condition (interfacial spin filtering) that suppresses backscattering for one spin projection while enhancing momentum relaxation for the other. The resulting resistance anisotropy peaks at an optimal metal thickness of a few nanometers, quantitatively reproducing the thickness and angular dependences typically attributed to spin Hall magnetoresistance (SMR), as well as its characteristic magnitude. Remarkably, the maximal AMR scales linearly with the smaller of the two coupling strengths - exchange or spin-orbit, highlighting a mechanism fundamentally distinct from SMR. Our scattering formulation maps onto Boltzmann boundary conditions and predicts other clear discriminants from SMR, including strong sensitivity to interfacial charge transfer and disorder.

cond-mat.mes-hall↗

arXiv2025

Adaptive Protein Design Protocols and Middleware

Computational protein design is experiencing a transformation driven by AI/ML. However, the range of potential protein sequences and structures is astronomically vast, even for moderately sized proteins. Hence, achieving convergence between generated and predicted structures demands substantial computational resources for sampling. The Integrated Machine-learning for Protein Structures at Scale (IMPRESS) offers methods and advanced computing systems for coupling AI to high-performance computing tasks, enabling the ability to evaluate the effectiveness of protein designs as they are developed, as well as the models and simulations used to generate data and train models. This paper introduces IMPRESS and demonstrates the development and implementation of an adaptive protein design protocol and its supporting computing infrastructure. This leads to increased consistency in the quality of protein design and enhanced throughput of protein design due to dynamic resource allocation and asynchronous workload execution.

cs.DC↗

arXiv2025

Integrating and Characterizing HPC Task Runtime Systems for hybrid AI-HPC workloads

Scientific workflows increasingly involve both HPC and machine-learning tasks, combining MPI-based simulations, training, and inference in a single execution. Launchers such as Slurm's srun constrain concurrency and throughput, making them unsuitable for dynamic and heterogeneous workloads. We present a performance study of RADICAL-Pilot (RP) integrated with Flux and Dragon, two complementary runtime systems that enable hierarchical resource management and high-throughput function execution. Using synthetic and production-scale workloads on Frontier, we characterize the task execution properties of RP across runtime configurations. RP+Flux sustains up to 930 tasks/s, and RP+Flux+Dragon exceeds 1,500 tasks/s with over 99.6% utilization. In contrast, srun peaks at 152 tasks/s and degrades with scale, with utilization below 50%. For IMPECCABLE.v2 drug discovery campaign, RP+Flux reduces makespan by 30-60% relative to srun/Slurm and increases throughput more than four times on up to 1,024. These results demonstrate hybrid runtime integration in RP as a scalable approach for hybrid AI-HPC workloads.

cs.DC↗

arXiv2025

Josephson Diode Effect from Nonequilibrium Current in a Superconducting Interferometer

We investigate the Josephson diode effect in a superconducting interferometer under nonequilibrium conditions. In contrast to its thermodynamic counterpart, which requires the simultaneous breaking of time-reversal and inversion symmetry, we demonstrate that a diode-like asymmetry of the critical current can emerge solely due to a dissipative current in the normal region of an otherwise symmetric Josephson junction. This effect is driven entirely by the nonequilibrium conditions, without the need for additional inversion symmetry breaking. Using the standard quasiclassical Keldysh Green's function formalism, we explicitly calculate the diode coefficient from the supercurrent-phase relation of the interferometer. Remarkably, within certain ranges of control parameters, such as applied voltage, temperature, and the geometric aspect ratio of the device, the diode coefficient can exceed its nominal perfect value.

cond-mat.supr-con↗

arXiv2025

Optical detection of 4-spin chiral interaction in a 2D honeycomb ferrimagnet

Broken inversion symmetry of magnetic lattice is normally described by Lifshitz invariants in micromagnetic energy functional. Three exceptions are the lattices with T$_\textrm{d}$, C$_\textrm{3h}$ and D$_\textrm{3h}$ point group symmetries. The inversion symmetry breaking of the corresponding magnets is described by more complex 4-spin chiral invariants that cannot be related to Dzyaloshinskii-Moriya interaction. Experimental detection of 4-spin chiral interactions is an important task that has yet to be performed. We propose that the 4-spin chiral interaction can be probed by energy selective magnon relaxation in two-dimensional ferromagnet Fe$_{3}$GeTe$_{2}$ that possess D$_\textrm{3h}$ point group symmetry.

cond-mat.mtrl-sci↗

arXiv2025

Deep RC: A Scalable Data Engineering and Deep Learning Pipeline

Significant obstacles exist in scientific domains including genetics, climate modeling, and astronomy due to the management, preprocess, and training on complicated data for deep learning. Even while several large-scale solutions offer distributed execution environments, open-source alternatives that integrate scalable runtime tools, deep learning and data frameworks on high-performance computing platforms remain crucial for accessibility and flexibility. In this paper, we introduce Deep Radical-Cylon(RC), a heterogeneous runtime system that combines data engineering, deep learning frameworks, and workflow engines across several HPC environments, including cloud and supercomputing infrastructures. Deep RC supports heterogeneous systems with accelerators, allows the usage of communication libraries like MPI, GLOO and NCCL across multi-node setups, and facilitates parallel and distributed deep learning pipelines by utilizing Radical Pilot as a task execution framework. By attaining an end-to-end pipeline including preprocessing, model training, and postprocessing with 11 neural forecasting models (PyTorch) and hydrology models (TensorFlow) under identical resource conditions, the system reduces 3.28 and 75.9 seconds, respectively. The design of Deep RC guarantees the smooth integration of scalable data frameworks, such as Cylon, with deep learning processes, exhibiting strong performance on cloud platforms and scientific HPC systems. By offering a flexible, high-performance solution for resource-intensive applications, this method closes the gap between data preprocessing, model training, and postprocessing.

cs.DC↗

arXiv2025

Scalable Runtime Architecture for Data-driven, Hybrid HPC and ML Workflow Applications

Hybrid workflows combining traditional HPC and novel ML methodologies are transforming scientific computing. This paper presents the architecture and implementation of a scalable runtime system that extends RADICAL-Pilot with service-based execution to support AI-out-HPC workflows. Our runtime system enables distributed ML capabilities, efficient resource management, and seamless HPC/ML coupling across local and remote platforms. Preliminary experimental results show that our approach manages concurrent execution of ML models across local and remote HPC/cloud resources with minimal architectural overheads. This lays the foundation for prototyping three representative data-driven workflow applications and executing them at scale on leadership-class HPC platforms.

cs.DC↗

arXiv2024

Exascale Workflow Applications and Middleware: An ExaWorks Retrospective

Exascale computers offer transformative capabilities to combine data-driven and learning-based approaches with traditional simulation applications to accelerate scientific discovery and insight. However, these software combinations and integrations are difficult to achieve due to the challenges of coordinating and deploying heterogeneous software components on diverse and massive platforms. We present the ExaWorks project, which addresses many of these challenges. We developed a workflow Software Development Toolkit (SDK), a curated collection of workflow technologies that can be composed and interoperated through a common interface, engineered following current best practices, and specifically designed to work on HPC platforms. ExaWorks also developed PSI/J, a job management abstraction API, to simplify the construction of portable software components and applications that can be used over various HPC schedulers. The PSI/J API is a minimal interface for submitting and monitoring jobs and their execution state across multiple and commonly used HPC schedulers. We also describe several leading and innovative workflow examples of ExaWorks tools used on DOE leadership platforms. Furthermore, we discuss how our project is working with the workflow community, large computing facilities, and HPC platform vendors to address the requirements of workflows sustainably at the exascale.

cs.SE↗

arXiv2024

ExaWorks Software Development Kit: A Robust and Scalable Collection of Interoperable Workflow Technologies

Scientific discovery increasingly requires executing heterogeneous scientific workflows on high-performance computing (HPC) platforms. Heterogeneous workflows contain different types of tasks (e.g., simulation, analysis, and learning) that need to be mapped, scheduled, and launched on different computing. That requires a software stack that enables users to code their workflows and automate resource management and workflow execution. Currently, there are many workflow technologies with diverse levels of robustness and capabilities, and users face difficult choices of software that can effectively and efficiently support their use cases on HPC machines, especially when considering the latest exascale platforms. We contributed to addressing this issue by developing the ExaWorks Software Development Kit (SDK). The SDK is a curated collection of workflow technologies engineered following current best practices and specifically designed to work on HPC platforms. We present our experience with (1) curating those technologies, (2) integrating them to provide users with new capabilities, (3) developing a continuous integration platform to test the SDK on DOE HPC platforms, (4) designing a dashboard to publish the results of those tests, and (5) devising an innovative documentation platform to help users to use those technologies. Our experience details the requirements and the best practices needed to curate workflow technologies, and it also serves as a blueprint for the capabilities and services that DOE will have to offer to support a variety of scientific heterogeneous workflows on the newly available exascale HPC platforms.

cs.SE↗

arXiv2024

Design and Implementation of an Analysis Pipeline for Heterogeneous Data

Managing and preparing complex data for deep learning, a prevalent approach in large-scale data science can be challenging. Data transfer for model training also presents difficulties, impacting scientific fields like genomics, climate modeling, and astronomy. A large-scale solution like Google Pathways with a distributed execution environment for deep learning models exists but is proprietary. Integrating existing open-source, scalable runtime tools and data frameworks on high-performance computing (HPC) platforms is crucial to address these challenges. Our objective is to establish a smooth and unified method of combining data engineering and deep learning frameworks with diverse execution capabilities that can be deployed on various high-performance computing platforms, including cloud and supercomputers. We aim to support heterogeneous systems with accelerators, where Cylon and other data engineering and deep learning frameworks can utilize heterogeneous execution. To achieve this, we propose Radical-Cylon, a heterogeneous runtime system with a parallel and distributed data framework to execute Cylon as a task of Radical Pilot. We thoroughly explain Radical-Cylon's design and development and the execution process of Cylon tasks using Radical Pilot. This approach enables the use of heterogeneous MPI-communicators across multiple nodes. Radical-Cylon achieves better performance than Bare-Metal Cylon with minimal and constant overhead. Radical-Cylon achieves (4~15)% faster execution time than batch execution while performing similar join and sort operations with 35 million and 3.5 billion rows with the same resources. The approach aims to excel in both scientific and engineering research HPC systems while demonstrating robust performance on cloud infrastructures. This dual capability fosters collaboration and innovation within the open-source scientific research community.

cs.DC↗

arXiv2024

Gilbert damping in two-dimensional metallic anti-ferromagnets

A finite spin life-time of conduction electrons may dominate Gilbert damping of two-dimensional metallic anti-ferromagnets or anti-ferromagnet/metal heterostructures. We investigate the Gilbert damping tensor for a typical low-energy model of a metallic anti-ferromagnet system with honeycomb magnetic lattice and Rashba spin-orbit coupling for conduction electrons. We distinguish three regimes of spin relaxation: exchange-dominated relaxation for weak spin-orbit coupling strength, Elliot-Yafet relaxation for moderate spin-orbit coupling, and Dyakonov-Perel relaxation for strong spin-orbit coupling. We show, however, that the latter regime takes place only for the in-plane Gilbert damping component. We also show that anisotropy of Gilbert damping persists for any finite spin-orbit interaction strength provided we consider no spatial variation of the Néel vector. Isotropic Gilbert damping is restored only if the electron spin-orbit length is larger than the magnon wavelength. Our theory applies to MnPS3 monolayer on Pt or to similar systems.

cond-mat.dis-nn↗

arXiv2024

Workflow Mini-Apps: Portable, Scalable, Tunable & Faithful Representations of Scientific Workflows

Workflows are critical for scientific discovery. However, the sophistication, heterogeneity, and scale of workflows make building, testing, and optimizing them increasingly challenging. Furthermore, their complexity and heterogeneity make performance reproducibility hard. In this paper, we propose workflow mini-apps as a tool to address the challenges in building and testing workflows while controlling the fidelity of representing realworld workflows. Workflow mini-apps are deployed and run on various HPC systems and architectures without workflow-specific constraints. We offer insight into their design and implementation, providing an analysis of their performance and reproducibility. Workflow mini-apps thus advance the science of workflows by providing simple, portable, and managed (fidelity) representations of otherwise complex and difficult-to-control real workflows.

cs.DC↗

arXiv2023

The Common Workflow Scheduler Interface: Status Quo and Future Plans

Nowadays, many scientific workflows from different domains, such as Remote Sensing, Astronomy, and Bioinformatics, are executed on large computing infrastructures managed by resource managers. Scientific workflow management systems (SWMS) support the workflow execution and communicate with the infrastructures' resource managers. However, the communication between SWMS and resource managers is complicated by a) inconsistent interfaces between SMWS and resource managers and b) the lack of support for workflow dependencies and workflow-specific properties. To tackle these issues, we developed the Common Workflow Scheduler Interface (CWSI), a simple yet powerful interface to exchange workflow-related information between a SWMS and a resource manager, making the resource manager workflow-aware. The first prototype implementations show that the CWSI can reduce the makespan already with simple but workflow-aware strategies up to 25%. In this paper, we show how existing workflow resource management research can be integrated into the CWSI.

cs.DC↗

arXiv2023

Nonequilibrium magnons from hot electrons in antiferromagnetic systems

We describe a \emph{nonthermal} magnon activation mechanism in antiferromagnetic (AFM) systems via locally equilibrated \emph{spin-unpolarized} hot electrons excited by an ultrafast intense laser pulse. We employ a quantum kinetic equation that takes into account a direct electron-magnon scattering channel in either bulk AFM metal or at the interface of the AFM/normal-metal heterostructure. The mechanism is responsible for the nonequilibrium population of AFM magnon modes on a subnanosecond timescale, which are formed shortly after the local thermalization of hot electrons by Coulomb interactions. Nonequilibrium magnon populations can be additionally manipulated by applying an external magnetic field. Our work paves the way toward spin dynamics control in AFM systems via the ultrafast manipulation of out-of-equilibrium magnon excitations.

cond-mat.mes-hall↗