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At least 469 records · Page 26

Compact scalars at the cosmological collider

Abstract We study the dynamics of scalar fields with compact field spaces, or axions, in de Sitter space. We argue that the field space topology can qualitatively affect the physics of these fields beyond just which terms are allowed in their actions. We argue that the sharpest difference is for massless fields — the free massless noncompact scalar field does not admit a two-point function that is both de Sitter-invariant and well-behaved at long distances, while the massless compact scalar does. As proof that this difference can be observable, we show that the long-distance behavior of a heavy scalar field, and thus its cosmological collider signal, can qualitatively change depending on whether it interacts with a light compact or noncompact scalar field. We find an interesting interplay between the circumference of the field space and the Hubble scale. When the field space is much larger than Hubble, the compact field behaves similarly to a light noncompact field and forces the heavy field to dilute much faster than any free field can. However, depending on how much smaller the field space is compared to Hubble, the compact field can cause the heavy scalar to decay either faster or slower than any free field and so we conclude that there can be qualitative and observable consequences of the field space’s topology in inflationary correlation functions.

Physics↗

Detection of Forced Change Within Combined Climate Fields Using Explainable Neural Networks

Abstract Assessing forced climate change requires the extraction of the forced signal from the background of climate noise. Traditionally, tools for extracting forced climate change signals have focused on one atmospheric variable at a time, however, using multiple variables can reduce noise and allow for easier detection of the forced response. Following previous work, we train artificial neural networks to predict the year of single‐ and multi‐variable maps from forced climate model simulations. To perform this task, the neural networks learn patterns that allow them to discriminate between maps from different years—that is, the neural networks learn the patterns of the forced signal amidst the shroud of internal variability and climate model disagreement. When presented with combined input fields (multiple seasons, variables, or both), the neural networks are able to detect the signal of forced change earlier than when given single fields alone by utilizing complex, nonlinear relationships between multiple variables and seasons. We use layer‐wise relevance propagation, a neural network explainability tool, to identify the multivariate patterns learned by the neural networks that serve as reliable indicators of the forced response. These “indicator patterns” vary in time and between climate models, providing a template for investigating inter‐model differences in the time evolution of the forced response. This work demonstrates how neural networks and their explainability tools can be harnessed to identify patterns of the forced signal within combined fields.

54 ENVIRONMENTAL SCIENCES↗

Rigorous constraints on three-nucleon forces in chiral effective field theory from fast and accurate calculations of few-body observables

We explore the constraints on the three-nucleon force (3NF) of chiral effective field theory (χ EFT) that are provided by bound-state observables in the A = 3 and A = 4 sectors. Our statistically rigorous analysis incorporates experimental error, computational method uncertainty, and the uncertainty due to truncation of the χ EFT expansion at next-to-next-to-leading order. A consistent solution for the 3 H binding energy, the 4 He binding energy and radius, and the 3 H β-decay rate can only be obtained if χ EFT truncation errors are included in the analysis. Here, the β-decay rate is the only one of these that yields a nondegenerate constraint on the 3NF low-energy constants, which makes it crucial for the parameter estimation. We use eigenvector continuation for fast and accurate emulation of no-core shell model calculations of the few-nucleon observables. This facilitates sampling of the posterior probability distribution, allowing us to also determine the distributions of the parameters that quantify the truncation error. We find a χ EFT expansion parameter of Q = 0.33 ± 0.06 for these observables.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Sequential Dual Alignments Introduce Synergistic Effect on Hexagonal Boron Nitride Platelets for Superior Thermal Performance

Planarly aligning 2D platelets is challenging due to their additional orientational freedom compared to 1D materials. Here, this study reports a sequential dual-alignment approach, employing an extrusion-printing-induced shear force and rotating-magnetic-field-induced force couple for platelet planarly alignment in a yield-stress support bath. It is hypothesized that the partial alignment induced by a directional shear force facilitates subsequent axial rotation of the platelets for planar alignment under an external force couple, resulting in a synergistic alignment effect. This sequential dual-alignment approach achieves better planar alignment of 2D modified hexagonal boron nitride (mhBN). Specifically, the thermal conductivity of the 40 wt% mhBN/epoxy composite is significantly higher (692%) than that of unaligned composites, surpassing the cumulative effect of individual methods (only 133%) with a 5 times more synergistic effect. For 30, 40, and 50 wt% mhBN composites, the thermal conductivity values (5.9, 9.5, and 13.8 W m –1 K –1 ) show considerable improvement compared to the previously reported highest values (5.3, 6.6, and 8.6 W m –1 K –1 ). Additionally, a 3D mhBN/epoxy heat sink is printed and evaluated to demonstrate the feasibility of device fabrication. The approach enables the planar alignment of electrically or thermally conducting 2D fillers during 3D fabrication.

36 MATERIALS SCIENCE↗

Evaluation of Mode Meters Robust to Forced Oscillations using Field-Measured Data

Mode meters are tools used by power system operators to continuously monitor a system's small-signal stability. They do so by estimating the system's electromechanical modes of oscillation. When a system undergoes a forced oscillation, mode meters may become biased because the two types of oscillation cannot be distinguished. Modified mode meter algorithms robust to this bias have been proposed in prior research, but these studies were based primarily on simulated data. In this paper, modified least squares and Yule-Walker mode meter algorithms are evaluated using field-measured data from phasor measurement units (PMUs). Results show that the sensitivities of the least squares algorithm make it impractical for use given the complexities of real-world forced oscillations. However, the modified Yule-Walker algorithm is shown to perform well and has significant potential for practical deployment in mode meter tools.

Follum, James D.↗

A smooth contact algorithm for the combined finite discrete element method

From its inception, the combined finite discrete element method has used a distributed potential contact force algorithm to resolve interaction between finite elements. The contact interaction algorithm relies on evaluation of the contact force potential field. The problem with existing algorithms is that the potential field introduces artificial numerical non-smoothness in the contact force. This work introduces a smooth potential field based on the finite element topology, and a generalized contact interaction law is constructed on top of the smooth potential field. Further, a number of validation cases for the proposed algorithm, considering different shapes of discrete elements, are presented, and detailed aspects of the proposed contact interaction law are tested with numerical examples.

97 MATHEMATICS AND COMPUTING↗

Ponderomotive recoil for electromagnetic waves

When waves damp or amplify on resonant particles in a plasma, nonresonant particles experience a recoil force that conserves the total momentum between particles and electromagnetic fields. This force is important to understand, as it can completely negate current drive and rotation drive mechanisms that are predicted on the basis of only resonant particles. Here, the existing electrostatic theory of this recoil force is extended to electromagnetic waves. While the result bears close similarity to historical fluid theories of laser–plasma interactions, it now incorporates both resonant and nonresonant particles, allowing momentum conservation to be self-consistently proven. Furthermore, the result is shown to be generally valid for kinetic plasmas, which is verified through single-particle hot-plasma simulations. Here, the new form of the force provides physical insight into the nature of the generalized Minkowski (plasmon) momentum of geometrical optics, which is shown to correspond to the momentum gained by the field and nonresonant particles as the wave is self-consistently ramped up from vanishing amplitude.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

On the meaning of Berry force for unrestricted systems treated with mean-field electronic structure

We show that the Berry force as computed by an approximate, mean-field electronic structure can be meaningful if properly interpreted. In particular, for a model Hamiltonian representing a molecular system with an even number of electrons interacting via a two-body (Hubbard) interaction and a spin–orbit coupling, we show that a meaningful nonzero Berry force emerges whenever there is spin unrestriction—even though the Hamiltonian is real-valued and formally the on-diagonal single-surface Berry force must be zero. Moreover, if properly applied, this mean-field Berry force yields roughly the correct asymptotic motion for scattering through an avoided crossing. That being said, within the context of a ground-state calculation, several nuances do arise as far interpreting the Berry force correctly, and as a practical matter, the Berry force diverges near the Coulson–Fischer point (which can lead to numerical instabilities). Here, we do not address magnetic fields here.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Significant reduction in the low-field magnetization of Nb 3 Sn superconducting strands using the internal oxidation APC approach

Nb 3 Sn superconductors are promising for building accelerator magnets for future energy-frontier circular colliders. A critical factor for this application is the low-field persistent-current magnetization because it leads to several critical issues: e.g. low-field instability (including flux jumps), hysteresis loss, and field errors in magnet bores. Suppression of low-field magnetization requires reduction of low-field critical current density (J c ) or effective subelement size (d eff ). However, reduction of deff of state-of-the-art Nb 3 Sn conductors—the restacked-rod-process (RRP ® ) type—below 40–50 μm without a pronounced decrease in high-field J c is difficult. On the other hand, the internal oxidation method which forms artificial pinning centers (APC) in Nb 3 Sn offers an alternative approach to reducing the low-field magnetization. Compared with a conventional Nb 3 Sn conductor whose flux pinning force versus field (F p –B) curve peaks at ~20% of its irreversibility field (B irr ), the F p –B curve peaks of APC conductors shift to higher fields due to the point pinning effect, leading to flattening of the J c –B curves. The goal of this paper is to quantitatively study how much the APC approach can reduce the low-field magnetization. Here we measured the J c –B curves of an RRP ® conductor and two APC conductors (reacted at 700 °C) from zero field to B irr using a high-field vibrating sample magnetometer. Here the results showed that the APC conductors have higher non-Cu J c at high fields (e.g. 32%–41% higher at 16 T) and simultaneously lower non-Cu Jc at low fields (e.g. 28%–34% lower at 1 T) compared with the RRP ® . This effect is due to a competition between their Nb3Sn layer fraction ratios and layer F p ratios. Suppose they reach the same 16 T non-Cu J c , then the 1 T non-Cu J c and magnetization of the APC conductors are only half or even less compared with the RRP ® conductor.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Final Design and Analysis of the Superconducting Magnets for the Material Plasma Exposure Experiment

The Material Plasma Exposure eXperiment (MPEX), which has completed its final design, is a new linear plasma device to advance the understanding of plasma-material interactions through the generation and delivery of plasmas as they are expected in future fusion reactor divertors. MPEX will be a steady-state device to study high-fluence exposures of plasma-facing materials and components. The requirements for the magnetic field at the heating stages and the target make the application of superconducting coils necessary. The final designs for the superconducting magnets have been developed using three cryostat designs with warm bore diameters of 65 cm and 156 cm. The large bores are required to for other systems such as vacuum, water cooling, and RF power. There are 19 superconducting coils in MPEX that are contained in six cryostats. Although design, fabrication, and testing for the magnets as stand-alone units are straightforward, challenges will arise during the integration of the system. Various field profiles will be used during operation. The magnetic field where the electron cyclotron heating occurs needs to operate at both 1.25 and 2.50 T. Analyses that have been performed on the final design include designing the coils and determining operating currents to meet field requirements, forces between cryostats, the effects of coil and/or cryostat movement, and quench analysis. To ensure that the magnetic field requirements are met, a plan for monitoring the magnetic field at specified locations has been developed which includes the effect of coil and/or cryostat movement.

Burkhardt, Earle↗

Magnetic reconnection and thermal equilibration

When a magnetic field is forced to evolve on a time scale sev, as by footpoint motions driving the solar corona or non-axisymmetric instabilities in tokamaks, the magnetic field lines undergo large-scale changes in topology on a time scale approximately an order of magnitude longer than $\tau$ ev . But the physics that allows such changes operates on a time scale eight or more orders of magnitude slower. An analogous phenomenon occurs in air. Temperature equilibration occurs on a time scale approximately an order of magnitude longer than it takes air to cross a room, $\tau$ ev , although the physical mechanism that allows temperature equilibration is approximately four orders of magnitude slower than $\tau$ ev . The use of Lagrangian coordinates allows the fundamental equations to be solved and both phenomena explained. The theories of thermal equilibration and magnetic reconnection are developed in parallel to help readers obtain an understanding of the importance and implications of analyses using Lagrangian coordinates.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Optical Forces on an Oscillating Dipole Near VO2 Phase Transition

We investigate optical forces on oscillating dipoles close to a phase change vanadium dioxide (VO2) film, which exhibits a metal-insulator transition around 340 K and low thermal hysteresis. This configuration emulates the interaction between an illuminated nanosphere and an interface and we employ a classical description to capture its important aspects. We consider both electric and magnetic dipoles for two different configurations, namely with the dipole moments parallel and perpendicular to the VO2 film. By using Bruggeman theory to describe the effective optical response of the material, we show that the thermal hysteresis present in the VO2 transition clearly shows up in the behavior of optical forces. In the near-field regime, the force on both dipoles can change from attractive to repulsive just by heating (or cooling) the film for a selected frequency range. We also verified that the optical forces are comparable to the Casimir-Polder force in a similar system, revealing the possibility of modulating or even changing the sign of the resultant force on an illuminated nano-object due to the presence of a thermochromic material. We hope that this work contributes to set the grounds for alternative approaches to control light-matter interactions using phase-change materials.

36 MATERIALS SCIENCE↗

Machine Learning Interatomic Potentials for Modeling Framework Flexibility and Water Uptake in NbOFFIVE-1-Ni Metal–Organic Framework

Metal–organic frameworks (MOFs), with their distinctive porous structures and tunable chemical properties, have shown immense promise in the separation and storage of gases. Currently, the accurate simulation of their adsorptive properties remains challenging, especially for systems where the molecules fit very tightly into the pores. Traditional simulation methods often approximate the frameworks as rigid and do not account for the framework flexibility seen in materials such as NbOFFIVE-1-Ni. First-principles molecular dynamics (FPMD) simulations offer the desired accuracy in modeling this flexibility but are limited by their extensive computational demands, rendering them impractical for long simulations. Conversely, classical force field-based simulations offer computational efficiency but lack the necessary accuracy. Here, to break this accuracy-efficiency trade-off, we have developed machine learning interatomic potentials trained on energies and forces from FPMD to model the framework flexibility of NbOFFIVE-1-Ni in the presence of water over nanosecond time scales. Furthermore, by integrating MLIP-driven molecular dynamics (MLIP-MD) with grand canonical Monte Carlo (GCMC) simulations, we further incorporated framework flexibility into adsorption predictions, yielding water adsorption isotherms that better align with experimental data compared to those of conventional GCMC simulations. These advances offer new opportunities for the design and optimization of MOFs in gas storage and separation applications.

adsorption↗

The confluence of machine learning and multiscale simulations

Multiscale modeling has a long history of use in structural biology, as computational biologists strive to overcome the time- and length-scale limits of atomistic molecular dynamics. Contemporary machine learning techniques, such as deep learning, have promoted advances in virtually every field of science and engineering and are revitalizing the traditional notions of multiscale modeling. Deep learning has found success in various approaches for distilling information from fine-scale models, such as building surrogate models and guiding the development of coarse-grained potentials. However, perhaps its most powerful use in multiscale modeling is in defining latent spaces that enable efficient exploration of conformational space. In conclusion, this confluence of machine learning and multiscale simulation with modern high-performance computing promises a new era of discovery and innovation in structural biology.

59 BASIC BIOLOGICAL SCIENCES↗

Development and transferability of neural-network models for plasma-surface interactions

Plasma-surface interactions are increasingly critical to modern technologies; yet, accurate molecular dynamics simulations remain limited by the capabilities of interatomic potentials. Deep Potentials (DPs) promise to revolutionize the field by providing a systematic method for producing accurate interatomic potentials. The primary challenge of DP development is selecting a dataset, which efficiently spans the set of atomic environments one expects to encounter in the subsequent molecular dynamics simulations. The computational cost of density functional theory calculations, which are the typical basis for DP development, makes it impossible to directly verify the quality of a given DP. To address this challenge, we explore the development of a deep-learned interatomic potential, “DeepREBO,” trained to reproduce the behavior of the REBO2 empirical potential, enabling direct validation of training methodology and transferability. Using an active learning framework, we begin with a minimal dataset and iteratively expand it to train a Deep Potential-Smooth Edition model that faithfully reproduces REBO2 results for 25 eV hydrogen bombardment of diamond (001), a particularly challenging case. We show that small, carefully curated datasets can outperform large, unguided ones, with effective models requiring fewer than 15 000 snapshots. Subsequent transferability tests demonstrate that while DeepREBO generalizes well to diamond (111) surfaces, performance degrades for amorphous carbon or higher-energy impacts, highlighting the need for use-case-specific training data. We also evaluate methods to improve short-range repulsion. This study outlines best practices for training robust deep potentials and underscores the importance of dataset design for predictive plasma simulations.

Ab-initio molecular dynamics↗