Fluid Dynamics
Numerical Methods for a 2D "Bad" Boussinesq Equation: RK4, Strang Splitting, and High-frequency Fourier Modes
Arief Anbiya
Numerical methods for a two-dimensional “bad” Boussinesq equation: $u_{tt} = u_{xx} + u_{xxxx} + u_{yy} - 3 (u^{2})_{xx}$ are presented with good accuracy. The methods mainly depend on pseudo-spectral
Fourier with a trimming of carefully chosen high-frequency
Fourier modes. One method also relies on Runge-Kutta fourth order (RK4), and another method relies on Strang operator splitting. Before implementing the two methods, we analyze using
Fourier series the linearized version of the equation by removing the nonlinear term $3(u^{2})_{xx}$, and found that a particular bound or condition needs to be satisfied to avoid blow-up solution. We found that high-frequency
Fourier modes that do not satisfy the condition must be excluded from the
Fourier solution. We then apply this condition to the numerical methods for solving the nonlinear Boussinesq equation and found that including only the
Fourier modes that satisfy the condition gives stable numerical solution with good accuracy up to $t=100$. Including even just a few number of
Fourier modes that violate the condition can result in a blow-up solution as early as $t=23.5$. The accuracy of the method is measured by computing the $L^{\infty}$ error against a
soliton exact solution. The errors resulting from RK4 and Strang splitting numerical simulations differ slightly for small $\triangle t$, while there is a noticeable decrease in performance for the Strang splitting simulation as $\triangle t$ increases. Using our numerical methods, we also display a simulation with Dirichlet boundary condition to account for wave reflections.
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Popular Physics
The 1908 Tunguska event and some distant phenomena
Andrei Ol'khovatov
This paper is a continuation of a series of works, devoted to various aspects of the 1908 Tunguska event. This usually refers to an explosive phenomenon associated with the appearance of a forestfall, named nowadays as the Kulikovskii one. However, several other notable natural phenomena occurred in the Central Siberia on June 30, 1908. This paper considers geomorphological features, reports of substance discoveries, forestfalls, and earthquakes. The general conclusion is that the Tunguska event was a very complex phenomenon. Research of the Tunguska event requires the participation of experts in various fields.
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Physics and Society
The Singularity Warfare: The metatheoretical Framework
Ridvan Bari Urcosta
This paper introduces the "Singularity Warfare" concept, arguing that the accelerating pace of technological revolution, driven by artificial intelligence and
quantum mechanics, is fundamentally reshaping the nature of conflict. Moving beyond traditional "Newtonian" warfare and current military doctrines, this framework posits that future battlefields will be defined by a merger of physical and abstract domains, where human imagination and algorithmic logic become a unified, actionable reality. Victory will hinge on a unit's ability to maintain cognitive and technological "coherence" while creating "decoherence" in the adversary. The paper synthesizes theories from physics, philosophy, and futurology to provide a metatheoretical framework for understanding this paradigm shift.
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General Physics
New XOR & XNOR Operations in Instantaneous Noise-Based Logic
Nasir Kenarangui, Arthur Powalka and Laszlo Kish
Instantaneous Noise-Based Logic (INBL) presents a classical noise-based computing framework as an alternative to
quantum computation, though some logic gates remain unimplemented for achieving universality over superpositions. INBL encodes M noise-bits using 2M orthogonal
stochastic reference noises to construct a 2^M-dimensional product-based
Hilbert space (hyperspace). Vectors in this hyperspace correspond to products of reference noises representing bit values in M-bit binary strings. This work introduces INBL implementations of XOR and XNOR operations targeting specific bits, facilitating pairwise operations directly between strings of equal length or hyperspace vectors, which are the longest strings. These operations naturally extend to superpositions, potentially delivering significant improvements in computational speed and hardware complexity. Diverging from previous methods by Khreishah et al., our approach avoids direct manipulation of the reference noise system, enabling more flexible and general-purpose implementations. We validate INBL operations-including NOT, XOR, and XNOR-through simulation using random telegraph waves, demonstrating practical feasibility without explicit reference noise manipulation.
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Chemical Physics
VEDA: Generation of 3D Molecules via Variance-Exploding Diffusion with Annealing
Peining Zhang, Jinbo Bi and Minghu Song
Diffusion models show promise for 3D molecular generation, but face a fundamental trade-off between sampling efficiency and conformational accuracy. While flow-based models are fast, they often produce geometrically inaccurate structures, as they have difficulty capturing the
multimodal distributions of molecular conformations. In contrast, denoising
diffusion models are more accurate but suffer from slow sampling, a limitation attributed to sub-optimal integration between
diffusion dynamics and SE(3)-equivariant architectures. To address this, we propose VEDA, a unified SE(3)-equivariant framework that combines variance-exploding
diffusion with annealing to efficiently generate conformationally accurate 3D molecular structures. Specifically, our key technical contributions include: (1) a VE schedule that enables noise injection functionally analogous to simulated annealing, improving 3D accuracy and reducing relaxation energy; (2) a novel preconditioning scheme that reconciles the coordinate-predicting nature of SE(3)-equivariant networks with a residual-based
diffusion objective, and (3) a new arcsin-based scheduler that concentrates sampling in critical intervals of the logarithmic signal-to-noise ratio. On the QM9 and GEOM-DRUGS
datasets, VEDA matches the sampling efficiency of flow-based models, achieving state-of-the-art valency stability and validity with only 100 sampling steps. More importantly, VEDA's generated structures are remarkably stable, as measured by their relaxation energy during GFN2-xTB optimization. The median energy change is only 1.72 kcal/mol, significantly lower than the 32.3 kcal/mol from its architectural baseline, SemlaFlow. Our framework demonstrates that principled integration of VE
diffusion with SE(3)-equivariant architectures can achieve both high chemical accuracy and computational efficiency.
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Chemical Physics
Nanobubble size controls gas hydrate nucleation in supercooled water
Ramkhelavan Kanaujiya, Atanu K. Metya, Rajnish Kumar et al. (+1 more)
Gas hydrates are crystalline compounds formed when water molecules encapsulate guest gas molecules under high pressure and low temperatures. They have gained significant interest due to their potential as alternative energy resources and their applications in gas storage, transportation, and carbon sequestration. However, the fundamental mechanisms governing their formation, especially the influence of gas bubbles, remain poorly understood. In this study, we use molecular dynamics (MD) simulations to examine how methane nanobubble size modulates hydrate formation in supercooled water. Nanobubbles of different sizes are generated by modulating the methane concentration in a methane-water mixture during equilibration under high-temperature and low-pressure conditions, followed by quenching to low temperature and high pressure to induce gas hydrate nucleation and subsequent growth. The simulations reveal a strong correlation between nanobubble size and the extent of hydrate formation. Specifically, the extent of hydrate formation increases with bubble size in the small-to-intermediate regime. However, beyond a critical bubble size threshold, the hydrate formation efficiency declines. The work provides new molecular-level insight into how nanobubble size modulates gas hydrate nucleation and growth dynamics.
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All authors Ramkhelavan Kanaujiya, Atanu K. Metya, Rajnish Kumar, Tarak K Patra
Fluid Dynamics
Physics-based localization methodology for Data Assimilation by Ensemble Kalman Filter
Sarp Er and Marcello Meldi
A physics-based methodology for the determination of the localization function for the Ensemble Kalman Filter (EnKF) is proposed. The spatial features of such function evolve dynamically over time according to the relevant instantaneous flow features of the ensemble members with the objective, to reduce the computational cost of the Data Assimilation (DA) procedure when applied with solvers for Computational Fluid Dynamics (CFD). The validation of the methodology has been carried out by the analysis of two test cases exhibiting different features. This permits to investigate different physical features, tailored for each test case, which affect the localization function. The flow over a two-dimensional square cylinder at $Re=150$ is the first case investigated. It has been shown that the proposed localization procedure leads to a more cost-effective DA process by reducing the size of the assimilated regions while keeping the same level of accuracy. The capabilities of the methodology are further demonstrated by the investigation of the turbulent flow around a three-dimensional circular cylinder for $Re=3900$. Again, the methodology exhibits an excellent trade off in terms of accuracy versus computational requirements.
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Optics
Deep Learning Driven Enhancement of Optical Vortex Line Robustness in Atmospheric Turbulence
Dmitrii Tsvetkov, Danilo Gomes Pires and Natalia Litchinitser
The stability of optical
vortex structures in turbulent environments is critical for their applications in optical communication,
quantum information, and structured light technologies. Although topological invariants, such as crossings and linking numbers, are fundamentally invariant, recent studies reveal that their observed values deteriorate considerably in turbulent conditions due to environmental effects. In this study, we introduce an alternative approach based on the
geometric stability of three-dimensional singularity line shapes, demonstrating that shape-based tracing of singularities outperforms both topological and spectral methods in
turbulence. To test this concept, we propose Flower Beams, a novel class of structured optical fields featuring controllable petal-like singularity morphologies. We construct an 81-element optical alphabet and classify these structures after
turbulence using deep learning. Our findings reveal that shape-based tracing achieves classification accuracy exceeding 90% in the weaker
turbulence regimes and remains highly competitive even in stronger
turbulence, significantly outperforming spectral and
topology-based approaches. Experimental results confirm that the predicted shape stability holds in real-world conditions. This study stablishes the shape of the singularities' lines as a scalable and resilient alternative for structured light tracing and transmission, opening new avenues for
turbulence-robust-applications.
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Atmospheric and Oceanic Physics
Resolving Thermospheric Vertical Wind Ambiguities and Energy Processes
Jeffrey P. Thayer and Austin Coleman
This study applies a generalized vertical coordinate system approach alongside thermodynamic control volume analysis to explore the nuanced interpretations of energy transfer processes associated with vertical motion in the thermosphere. Using simulations from the TIEGCM V3.0 model, a key finding reveals that transforming vertical winds in height coordinates onto constant pressure surfaces contain a substantial lifting component; an aspect often overlooked in previous research. This distinction is critical for internal energy assessments, as vertical winds defined in pressure coordinates directly contribute to the adiabatic heating or cooling rates while only a portion of the vertical wind in height coordinates contributes to these energy changes. These differences are demonstrated through schematic representations of thermodynamic control volumes that model a column of thermospheric gas undergoing various energy transfer processes. Accurately determining the portion of observed vertical winds that affect internal energy requires clear understanding between height and pressure surfaces; a nontrivial challenge in observational contexts. Furthermore, applying the generalized vertical coordinate framework to airglow emissions in the upper thermosphere uncovers an inherent ambiguity: vertical winds inferred from airglow observations may not align with those defined on pressure or altitude surfaces. This insight suggests that discrepancies in the behavior and magnitude of vertical winds derived from FPI observations of red-line emissions in the upper thermosphere are due to poorly known relations between the emission structure relative to pressure or height surfaces.
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Plasma Physics
Enabling Integrated AI Control on DIII-D: A Control System Design with State-of-the-art Experiments
Andrew Rothstein, Hiro Joseph Farre-Kaga, Jalal Butt et al. (+7 more)
We present the design and application of a general
algorithm for Prediction And Control using MAchiNe learning (PACMAN) in DIII-D. Machine learing (ML)-based predictors and controllers have shown great promise in achieving regimes in which traditional controllers fail, such as tearing mode free scenarios, ELM-free scenarios and stable advanced tokamak conditions. The architecture presented here was deployed on DIII-D to facilitate the end-to-end implementation of advanced control experiments, from diagnostic processing to final actuation commands. This paper describes the detailed design of the
algorithm and explains the motivation behind each design point. We also describe several successful ML control experiments in DIII-D using this
algorithm, including a reinforcement learning controller targeting advanced non-inductive
plasmas, a wide-pedestal quiescent H-mode ELM predictor, an Alfvén Eigenmode controller, a Model Predictive Control
plasma profile controller and a state-machine Tearing Mode predictor-controller. There is also discussion on guiding principles for real-time machine learning controller design and implementation.
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All authors Andrew Rothstein, Hiro Joseph Farre-Kaga, Jalal Butt, Ricardo Shousha, Keith Erickson, Takuma Wakatsuki, Azarakhsh Jalalvand, Peter Steiner, Sangkyeun Kim, Egemen Kolemen
Medical Physics
Per-Lesion Radiomics Analysis of 68Ga-DOTA FAPI-46 and 18F-FDG PET/CT in Non-Small Cell Lung Cancer: A pilot Study
Setareh Hasanabadi, Maryam Cheraghi, Hossein Behnam Manesh et al. (+8 more)
This pilot study compares per-lesion radiomics features of [68Ga]-DOTA FAPI-46 and [18F]-FDG PET/CT in non-small
cell lung cancer (NSCLC) to explore complementary insights into intratumoral heterogeneity beyond conventional SUV metrics, aiming to enhance lesion characterization and clinical decision-making. A total of 28 PET/CT scans (14 [18F]-FDG and 14 [68Ga]-DOTA FAPI-46) were acquired for the initial staging of biopsy-confirmed NSCLC. A total of 81 co-localized lesions (lung: 21, mediastinal lymph nodes: 42, bone: 18) were segmented, with radiomics features extracted via PyRadiomics after IBSI-compliant preprocessing. Paired per-lesion comparisons used t-tests or Wilcoxon signed-rank tests with Benjamini-Hochberg FDR correction. Significant differences (adjusted P < 0.05) were identified across intensity, texture, and shape features. In lung lesions, FAPI showed lower first-order metrics but higher variance and GLCM contrast, suggesting stromal heterogeneity. Mediastinal lymph nodes had fewer differences, with FAPI exhibiting lower run percentage (GLRLM: -0.298, -6.196, P=1.45E-08). Bone lesions showed extensive variations, including reduced FAPI
entropy (e.g.,
Entropy: -1.743, -5.798, P=6.95E-08). Feature overlaps highlighted complementary stromal (FAPI) and metabolic (FDG) insights. This pilot study demonstrates that per-lesion radiomics can capture complementary biological information from FAPI and FDG PET in NSCLC, highlighting intratumoral heterogeneity and stromal activity not fully appreciated by conventional SUV-based metrics.
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All authors Setareh Hasanabadi, Maryam Cheraghi, Hossein Behnam Manesh, Mohadeseh Bayat, Mehrdad Bakhshayesh Karam, Andrea Corsi, Yazdan Salimi, Mohammad Saber Azimi, Abtin Doroudinia, Arezu Karami, Hossein Arabi
Atomic Physics · Cross-listed from quant-ph
Cold-Atom Buoy: A Differential Magnetic Sensing Technique in Cold Quadrupole Traps
Árpád Kurkó, Dávid Nagy, Alexandra Simon et al. (+7 more)
We present a differential technique for vector
magnetic sensing based on a cold-atom cloud in a
magnetic quadrupole trap. An external homogeneous
magnetic field displaces the trap center in a direction and magnitude proportional to the field. By reversing the quadrupole polarity between experimental shots and comparing the resulting cloud positions, we extract a differential displacement signal that is free from common-mode effects such as
gravity and weak
magnetic-field inhomogeneities. The signal is directionally proportional to the external field and requires only absorption imaging, without spectroscopic interrogation. Assuming micron-scale position resolution, the technique enables field resolution at the milli-Gauss level. It offers a practical tool for field compensation in magnetically sensitive experimental stages, bridging operational regimes from Earth-level fields to atomic magnetometry. A straightforward extension to full three-dimensional sensing is possible with only a minimal addition to standard cold-atom infrastructure.
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All authors Árpád Kurkó, Dávid Nagy, Alexandra Simon, Thomas W. Clark, András Dombi, Dániel Varga, Francis B. Williams, József Fortágh, Peter Domokos, András Vukics
Chemical Physics
Field theoretic atomistics: Learning thermodynamic and variational surrogate to density functional theory
Sambit Das, Bikash Kanungo, Arghadwip Paul et al. (+1 more)
The Hohenberg-Kohn (HK)
theorem – the bedrock of density functional theory (DFT) – establishes a universal map from the external potential to the energy. It also relates the electron density and atomic forces to the variation of the energy with the external potential. But the HK map is rarely utilized in atomistics, wherein interatomic potentials are defined using the molecular or
crystal structure rather than the external potential. As a break from this tradition, we present a field theoretic atomistics framework where the external potential assumes the central quantity. We machine learn the HK energy map while satisfying the thermodynamic limit. Further, we obtain both forces and electron density from the variation of the HK energy map, that are exact relations. Our models attain good accuracy across diverse
benchmarks and compete with state-of-the-art machine learned interatomic potentials. Through electron density, we predict accurate dipole and quadrupole moments, otherwise nontrivial for interatomic potentials. Our formulation paves the way for a scalable electronic structure surrogate to DFT.
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All authors Sambit Das, Bikash Kanungo, Arghadwip Paul, Vikram Gavini
Computational Physics · Cross-listed from cs.RO
CENIC: Convex Error-controlled Numerical Integration for Contact
Vince Kurtz and Alejandro Castro
State-of-the-art robotics simulators operate in discrete time. This requires users to choose a time step, which is both critical and challenging: large steps can produce non-physical artifacts, while small steps force the simulation to run slowly. Continuous-time error-controlled integration avoids such issues by automatically adjusting the time step to achieve a desired accuracy. But existing error-controlled integrators struggle with the stiff dynamics of contact, and cannot meet the speed and scalability requirements of modern robotics workflows. We introduce CENIC, a new variable-step integrator that brings together recent advances in convex time-stepping and error-controlled integration, inheriting benefits from both continuous integration and discrete time-stepping. CENIC runs at fast real-time rates comparable to discrete-time robotics simulators like MuJoCo, Drake and Isaac Sim, while also providing guarantees on accuracy and convergence.
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Optics
Ultrafast Pulse Retrieval from Partial FROG Traces Using Implicit Diffusion Models
Abhimanyu Borthakur, Jack Eden Hirschman and Sergio Carbajo
Ultrashort
laser pulses enable attosecond-scale measurements and drive breakthroughs across science and technology, but their routine use hinges on reliable pulse characterization. Frequency-Resolved Optical Gating (FROG) is a leading solution, forming a spectrogram by scanning the delay between two pulse replicas and recording the nonlinear signal spectrum. In online settings, however, dense delay-frequency scans are costly or impractical-especially for long pulses, wavelength regimes with limited spectrometer coverage (e.g., UV), or hardware with coarse resolution, yielding severely undersampled FROG traces. Existing reconstruction methods struggle in this regime-iterative
algorithms are computationally heavy, convolutional networks blur fine structure, and sequence models are unstable when inputs are discontinuous or sparse. We present a generative
diffusion framework tailored to recover ultrafast pulse intensity and phase from incomplete FROG measurements. Our model infers missing spectro-temporal content with high fidelity, enabling accurate
retrieval from aggressively downsampled inputs. On a simulated
benchmark of FROG-pulse pairs, the
diffusion approach surpasses strong CNN and Seq2Seq baselines in accuracy and stability while remaining efficient enough for near real-time deployment.
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Applied Physics
High-Purity Diamond Integration on $\beta$-Ga$_2$O$_3$ via Microwave Plasma CVD for Enhanced Thermal Management
Saleh Ahmed Khan, Stephen Margiotta, Ahmed Ibreljic et al. (+1 more)
The integration of diamond with $\beta$-Ga$_2$O$_3$ presents a promising pathway to enhance thermal management in high-power electronic devices, where the inherently low thermal conductivity of $\beta$-Ga$_2$O$_3$ can lead to localized self-heating and elevated junction temperatures. In this work, we demonstrate a scalable, low-damage approach for integrating polycrystalline diamond films on (010) $\beta$-Ga$_2$O$_3$ substrates via microwave
plasma chemical vapor deposition (MPCVD), employing dielectric interlayers and
polymer-assisted electrostatic nanodiamond seeding to systematically evaluate the impact of growth conditions on film morphology, grain
evolution, phase purity, and optical characteristics. At a growth temperature of 800$^\circ$C, progressive grain coarsening is observed with extended deposition, with the lateral grain size increasing from 37.6 nm (53 nm thickness) to 192.5 nm for an 886 nm-thick film. This microstructural
evolution is accompanied by narrowing of the diamond Raman peak and a monotonic increase in the sp$^3$ phase fraction from 95.9% to as high as 98.9%, indicating continued suppression of non-diamond carbon with prolonged growth. Comparison of SiO$_2$ and SiN$_x$ interlayers under identical growth conditions shows only marginal differences in grain size and phase purity, indicating limited interlayer influence once a high nucleation density is established. Importantly, diamond films exhibiting greater than 96% sp$^3$ phase content were achieved at substrate temperatures as low as 480$^\circ$C, highlighting the viability of diamond-on-Ga$_2$O$_3$ integration under reduced thermal budgets. These findings establish a robust and scalable platform for integrating diamond on $\beta$-Ga$_2$O$_3$, supporting the development of next-generation power and RF devices with improved thermal management.
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All authors Saleh Ahmed Khan, Stephen Margiotta, Ahmed Ibreljic, Anhar Bhuiyan
Fluid Dynamics
Patient-Scale Blood Flow Analysis in Artery Stent Implantation via Smoothed-Particle Hydrodynamics
Jinlei Zhou, Sukang Peng, Yongchuan Yu et al. (+2 more)
A unified Smoothed Particle Hydrodynamics (SPH) simulation framework for coronary stent implantation is developed, which unifies weakly compressible hemodynamics, Neo-Hookean solids, and stent-artery contacts, based on a multi-resolution particle discretization. Prior to application, feasibility and accuracy are established via three baseline validations: (i) poiseuille flow in a two-dimensional channel with prescribed
parabolic inflow and a pressure outlet, maintaining
parabolic profiles with low Root Mean Squared Error of Prediction (RMSEP); (ii) channel flow initialized with a uniform velocity field and driven by a specified inlet-outlet pressure differential, with agreement to reference profiles quantified by low RMSEP at five reference instants; and (iii) a three-ring impact
benchmark in solid mechanics, capturing large deformation, multi-body contact, and self-contact. The validated framework is subsequently applied to a coronary bifurcation with a focal stenosis, where flow-field diagnostics reveal acceleration at the stenotic throat, near-wall low-velocity zones, and co-localization of elevated pressure with increased Von Mises stress at the bifurcation and inlet. Following simulated stent implantation, velocity transitions across the stenosis become smoother, pressure gradients are reduced, and the fractional flow reserve increases from 0.45 to 0.91. These results demonstrate that the proposed SPH framework yields quantitatively reliable, clinically interpretable hemodynamic metrics alongside robust solid-solid contact predictions, thereby supporting rigorous analysis and pre-procedural planning of vascular interventions.
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All authors Jinlei Zhou, Sukang Peng, Yongchuan Yu, Dong Wu, Xiangyu Hu
Physics and Society
The Emergence of Socio-Economic Structure: A First-Principles Kinetic Theory
Miguel A. Durán-Olivencia
Bridging the gap between individual
agent behavior and macroscopic societal patterns is a central challenge in the social sciences. In this work, we propose a solution to this problem via a kinetic theory formulation. We demonstrate that complex, empirically-observed phenomena, such as the concentration of populations in cities and the emergence of power-law wealth distributions, can be derived directly from a microscopic model of
agents governed by underdamped Langevin dynamics. Our multi-scale derivation yields the exact mesoscopic fluctuating (Dean-Kawasaki) dynamics and the macroscopic Vlasov-Fokker-Planck system of equations. The analytical solution of this system reveals how a heterogeneous resource landscape alone is sufficient to generate the coupled structures of spatial and economic inequality, thus providing a formal link between micro-level stochasticity and macro-level deterministic order.
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Applied Physics · Cross-listed from cond-mat.mtrl-sci
Decoupling Composition and Band Gap in $\kappa$-Ga$_2$O$_3$ Heterostructures via STEM-EELS
Annett Thøgersen, Georg Muntingh, Lasse Vines et al. (+5 more)
High-resolution mapping of electronic properties at
oxide heterointerfaces remains challenging due to probe delocalization and overlapping signals. In this work, we employ monochromated, probe-corrected scanning transmission electron microscopy combined with electron energy-loss spectroscopy (STEM-EELS) to resolve band gap variations across $\kappa$-Ga$_2$O$_3$-based multilayers with nanometer-scale precision. A custom automated quantitative-based EELS analysis framework enabled automated band gap fitting and visualization, ensuring reproducibility and high spatial resolution. By optimizing acquisition parameters and quantifying inelastic delocalization, we demonstrate reliable extraction of band gap excitations from layers only a few nanometers thick. For
heterostructures grown on ITO templates, strain at defect-free interfaces induces a gradual band gap transition from $5.08~\mathrm{eV}$ to $4.28~\mathrm{eV}$ over $\sim 10~\mathrm{nm}$, despite an abrupt compositional change. In contrast, ZnO-based templates introduce structural defects that relieve strain, yielding band gaps consistent with composition. These results establish STEM-EELS as a powerful tool for nanoscale electronic characterization and highlight the dominant role of interfacial strain over composition in governing local band structure.
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All authors Annett Thøgersen, Georg Muntingh, Lasse Vines, Øystein Prytz, Max Kneiß, Marius Grundmann, Holger von Wenckstern, Ingvild J. T. Jensen
Chemical Physics · Cross-listed from quant-ph
Hybrid Atomistic-Parametric Decoherence Model for Molecular Spin Qubits
Katy Aruachan, Sanoj Raj, Yamil J. Colón et al. (+2 more)
Solid-state molecular
qubits with open-shell ground states have great potential for addressability, scalability, and tunability, but understanding the fundamental limits of
quantum coherence in these systems is challenging due to the complexity of the
qubit environment. To address this, we develop a random
Hamiltonian approach where the molecular $g$-tensor fluctuates due to classical
lattice motion obtained from molecular dynamics simulations at constant temperature. Atomistic $g$-tensor fluctuations are used to construct Redfield
quantum master equations that predict the relaxation $T_1$ and dephasing $T_2$ times of copper porphyrin
qubits in a crystalline framework. Assuming one-
phonon spin-
lattice interaction processes, $1/T$ temperature scaling and $1/B^3$
magnetic field scaling of $T_1$ are established using atomistic bath correlation functions. Atomistic $T_1$ predictions overestimate the available experimental data by orders of magnitude. Quantitative agreement with measurements at all
magnetic fields is restored by introducing a
magnetic field noise model to describe
lattice nuclear spins, with field-dependent noise amplitude in the range $\delta B\sim 10\,\mu{\rm T}- 1\,{\rm mT}$ for the copper porphyrin system. We show that while $T_1$ scales as $1/B$ experimentally due to a combination of
spin-
lattice and
magnetic noise contributions, $T_2$ scales strictly as $ 1/B^2$ due to low-frequency dephasing processes associated with
magnetic field noise. Our work demonstrates the potential of dynamical methods for modeling the open
quantum system dynamics of molecular
spin qubits.
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All authors Katy Aruachan, Sanoj Raj, Yamil J. Colón, Daniel Aravena, Felipe Herrera
Plasma Physics
Collisional stopping power of ions in warm dense matter
Lucas Babati, Shane Rightley, Nathaniel Shaffer et al. (+1 more)
A model for the collisional stopping of ions on free electrons in warm dense matter is developed and explored. It is based on
plasma kinetic theory, but with modifications to address the warm dense matter regime. Specifically, it uses the Boltzmann-Uehling-Uhlenbeck kinetic equation to incorporate effects of
Fermi degeneracy of electrons. The cross section is computed from
quantum scattering of electrons and ions occuring via the potential of mean force derived from an average atom model, which incorporates effects of strong Coulomb correlations. Predictions from this model show comparable accuracy to results from time-dependent density functional theory calculations for deuterium near solid density and a temperature of several electronvolts, at a fraction of the computational cost. Further, the model captures the transition of a
plasma from the classical limit to the degenerate limit, including qualitative behaviors of solid state theory.
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All authors Lucas Babati, Shane Rightley, Nathaniel Shaffer, Scott Baalrud