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An electrostatic sector device for a mass spectrometer is formed from a single piece of machinable ceramic. The machined ceramic is coated with a nickel coating, and a notch is etched in the nickel coating to form two separated portions. The sector can be covered by a cover formed from a separate piece of machined ceramic.
Most current machine learning interatomic potentials (MLIPs) rely on short-range approximations, without explicit treatment of long-range electrostatics. To address this, we recently developed the Latent Ewald Summation (LES) method, which infers electrostatic interactions, polarization, and Born effective charges (BECs), just by learning from energy and force training data. Here, in this study, we present LES as a standalone library, compatible with any short-range MLIP, and demonstrate its integration with methods such as MACE, NequIP, Allegro, CACE, CHGNet, and UMA. We benchmark LES-enhanced models on distinct systems, including bulk water, polar dipeptides, and gold dimer adsorption on defective substrates, and show that LES not only captures correct electrostatics but also improves accuracy. Additionally, we scale LES to large and chemically diverse data by training MACELES-OFF on the SPICE set containing molecules and clusters, making a universal MLIP with electrostatics for organic systems, including biomolecules. MACELES-OFF is more accurate than its short-range counterpart (MACE-OFF) trained on the same data set, predicts dipoles and BECs reliably, and has better descriptions of bulk liquids. By enabling efficient long-range electrostatics without directly training on electrical properties, LES paves the way for electrostatic foundation MLIPs.
Devices, methods, and systems for electrostatic machines which can act as both an electric motor, converting electrical energy to mechanical energy, and an electric generator, converting mechanical energy to electrical energy, are described. In some embodiments, a spring positioned between two oppositely charged plates may be used as an electric motor and/or electric generator.
We are developing a micro-machined electrostatically actuated Fabry-Perot tunable filter with a large clear aperture for application in high through-put wide-field imaging spectroscopy and lidar systems. In the first phase of this effort, we are developing key components based on coupled electro-mechanical simulations. In particular, the movable etalon plate design leverages high coating stresses to yield a flat surface in drum-head tension over a large diameter (12.5 mm). In this approach, the cylindrical silicon movable plate is back etched, resulting in an optically coated membrane that is suspended from a thick silicon support ring. Understanding the interaction between the support ring, suspended membrane, and coating is critical to developing surfaces that are flat to within stringent etalon requirements. In this work, we present the simulations used to develop the movable plate, spring suspension system, and electrostatic actuation mechanism. We also present results from tests of fabricated proof of concept components.
Machine learning models for the potential energy of multi-atomic systems, such as the deep potential (DP) model, make molecular simulations with the accuracy of quantum mechanical density functional theory possible at a cost only moderately higher than that of empirical force fields. However, the majority of these models lack explicit long-range interactions and fail to describe properties that derive from the Coulombic tail of the forces. To overcome this limitation, we extend the DP model by approximating the long-range electrostatic interaction between ions (nuclei + core electrons) and valence electrons with that of distributions of spherical Gaussian charges located at ionic and electronic sites. The latter are rigorously defined in terms of the centers of the maximally localized Wannier distributions, whose dependence on the local atomic environment is modeled accurately by a deep neural network. In the DP long-range (DPLR) model, the electrostatic energy of the Gaussian charge system is added to short-range interactions that are represented as in the standard DP model. The resulting potential energy surface is smooth and possesses analytical forces and virial. Missing effects in the standard DP scheme are recovered, improving on accuracy and predictive power. By including long-range electrostatics, DPLR correctly extrapolates to large systems the potential energy surface learned from quantum mechanical calculations on smaller systems. We illustrate the approach with three examples: the potential energy profile of the water dimer, the free energy of interaction of a water molecule with a liquid water slab, and the phonon dispersion curves of the NaCl crystal.
In this work, we explore the role of long-range interactions in atomistic machine-learning models by analyzing the effects on fitting accuracy, isolated cluster properties, and bulk thermodynamic properties. Such models have become increasingly popular in molecular simulations given their ability to learn highly complex and multi-dimensional interactions within a local environment; however, many of them fundamentally lack a description of explicit long-range interactions. In order to provide a well-defined benchmark system with precisely known pairwise interactions, we chose as the reference model a flexible version of the Extended Simple Point Charge (SPC/E) water model. Our analysis shows that while local representations are sufficient for predictions of the condensed liquid phase, the short-range nature of machine-learning models falls short in representing cluster and vapor phase properties. These findings provide an improved understanding of the role of long-range interactions in machine learning models and the regimes where they are necessary.
In this work we perform the first demonstration of gear-changing in a real world collider. Gear-changing refers to a collision scheme where each ring of a collider stores a different harmonic number of bunches. These bunches are kept synchronized using different velocities. Such a system has been theorized, but has now been demonstrated using the Double ElectroStatic Ion Ring ExpEriment (DESIREE) in Stockholm Sweden. The experiment was able to demonstrate a gear-changing system, with both four on three bunches and five on four bunches. We determined a measurable parameter that shows a gear-changing system out to $37500$ turns of the slow beam (1 second). We were also able to detect a measurable longitudinal beam-beam interaction. We developed insights into how to control this type of system, opening up new possibilities, and showing that DESIREE can be used for gear-changing research.
The potential applications of fusion reactors, the desirable properties of reactors intended for various applications, and the limitations of the Tokamak concept are discussed. The principles and characteristics of 20 distinct alternative confinement concepts are described, each of which may be an alternative to the Tokamak. The devices are classed as Tokamak-like, stellarator-like, mirror machines, bumpy tori, electrostatically assisted, migma concept, and wall-confined plasma.
The development of heat-resistant dielectric polymers that withstand intense electric fields at high temperatures is critical for electrification. Balancing thermal stability and electrical insulation, however, is exceptionally challenging as these properties are often inversely correlated. A traditional intuition-driven polymer design approach results in a slow discovery loop that limits breakthroughs. Here we present a machine learning-driven strategy to rapidly identify high-performance, heat-resistant polymers. A trustworthy feed-forward neural network is trained to predict key proxy parameters and down select polymer candidates from a library of nearly 50,000 polysulfates. The highly efficient and modular sulfur fluoride exchange click chemistry enables successful synthesis and validation of selected candidates. A polysulfate featuring a 9,9-di(naphthalene)-fluorene repeat unit exhibits excellent thermal resilience and achieves ultrahigh discharged energy density with over 90% efficiency at 200 °C. Its exceptional cycling stability underscores its promise for applications in demanding electrified environments.
Despite advancements in high-performance computing and modern numerical algorithms, computational cost remains prohibitive for multi-query kinetic plasma simulations. Here, in this work, we develop data-driven reduced-order models (ROMs) for collisionless electrostatic plasma dynamics, based on the kinetic Vlasov-Poisson equation. Our ROM approach projects the equation onto a linear subspace defined by the proper orthogonal decomposition (POD) modes. We introduce an efficient tensorial method to update the nonlinear term using a precomputed third-order tensor. We capture multiscale behavior with a minimal number of POD modes by decomposing the solution manifold into multiple time windows and creating temporally local ROMs. We consider two strategies for decomposition: one based on the physical time and the other based on the electric field energy. Applied to the 1D1V Vlasov–Poisson simulations, that is, prescribed E-field, Landau damping, and two-stream instability, we demonstrate that our ROMs accurately capture the total energy of the system both for parametric and time extrapolation cases. The temporally local ROMs are more efficient and accurate than the single ROM. In addition, in the two-stream instability case, we show that the energy-windowing reduced-order model (EW-ROM) is more efficient and accurate than the time-windowing reduced-order model (TW-ROM). With the tensorial approach, EW-ROM solves the equation approximately 90 times faster than Eulerian simulations while maintaining a maximum relative error of 7.5% for the training data and 11% for the testing data.
We describe a mechanism by which the aberration content of a focused, multiterawatt laser is imprinted upon the forward angular distribution of electrons ionized within and ponderomotively expelled from the focal volume. In our experiments, the laser aberration type and magnitude are controllably varied, and the measured electron distributions are correspondingly modified in a way consistent with predictions from numerical simulations. This imprint mechanism shows potential for enabling the development of accurate focal-spot characterization of intense lasers when fired at full power and is being developed for extension to petawatt laser systems.
Elliptic partial differential equations (PDEs) can model many physical phenomena, such as electrostatics, acoustics, wave propagation, and diffusion. In scientific machine learning settings, a high-throughput PDE solver may be required to generate a training dataset, run in the inner loop of an iterative algorithm, or interface directly with a deep neural network. To provide value to machine learning users, such a PDE solver must be compatible with standard automatic differentiation frameworks, scale efficiently when run on graphics processing units (GPUs), and maintain high accuracy for a large range of input parameters. We have designed the jaxhps package with these use-cases in mind by implementing a highly efficient and accurate solver for elliptic problems with native hardware acceleration and automatic differentiation support.
Researchers designed and constructed a novel electron tunnel sensor which takes advantage of the mechanical properties of micro-machined silicon. For the first time, electrostatic forces are used to control the tunnel electrode separation, thereby avoiding the thermal drift and noise problems associated with piezoelectric actuators. The entire structure is composed of micro-machined silicon single crystals, including a folded cantilever spring and a tip. The application of this sensor to the development of a sensitive accelerometer is described.
The damping of electrostatic ion-cyclotron waves by ion-neutral collisions was studied in a single-ended Q machine. The amplitudes of K(+) and Cs(+) electrostatic ion-cyclotron waves were measured as a function of neutral pressure in helium, neon, argon, krypton, and xenon. For each ion/neutral atom combination, the electrostatic ion-cyclotron wave amplitude maximizes at a neutral pressure that scales monotonically with the m(+)/m(n) mass ratio. This result is interpreted by considering the dynamics of elastic collisions between the ions and the neutral atoms.
Electrostatic forces strongly influence the behavior of granular materials in both dispersed (cloud) systems and semi-packed systems. These forces can cause aggregation or dispersion of particles and are important in a variety of astrophysical and planetary settings. There are also many industrial and commercial settings where granular matter and electrostatics become partners for both good and bad. This partnership is important for human exploration on Mars where dust adheres to suits, machines, and habitats. Long-range Coulombic (electrostatic) forces, as opposed to contact-induced dipoles and van der Waals attractions, are generally regarded as resulting from net charge. We have proposed that in addition to net charge interactions, randomly distributed charge carriers on grains will result in a dipole moment regardless of any net charge. If grains are unconfined, or fluidized, they will rotate so that the dipole always induces attraction between grains. Aggregates are readily formed, and Coulombic polarity resulting from the dipole produces end-to-end stacking of grains to form filamentary aggregates. This has been demonstrated in USML experiments on Space Shuttle where microgravity facilitated the unmasking of static forces. It has also been demonstrated in a computer model using grains with charge carriers of both sign. Model results very closely resembled micro-g results with actual sand grains. Further computer modeling of the aggregation process has been conducted to improve our understanding of the aggregation process, and to provide a predictive tool for microgravity experiments slated for Space Station. These experiments will attempt to prove the dipole concept as outlined above. We have considerably enhanced the original computer model: refinements to the algorithm have improved the fidelity of grain behavior during grain contact, special attention has been paid to simulation time steps to enable establishment of a meaningful, quantitative time axis, and calibration of rounding accuracies have been conducted to test cumulative numerical influences in the model. The model has been run for larger grain populations, variable initial cloud densities, and we have introduced random net charging to individual grains, as well as a net charge to the cloud as a whole. The model uses 3 positive and 3 negative charges randomly distributed on each grain, with up to 160 grains contained within various size "boxes" that define the initial number densities in the clouds. Each charge represents localized charged region on a grain, but does not necessarily imply single quantized charge carriers. The Coulomb equations are then allowed to interact for each monopole: dipoles and any higher order charge coupling is a natural product of these "free" interactions over which the modeler exerts no influence. The charges are placed on surfaces of grains at random locations. A series of runs was conducted for neutral grains that had a perfect balance of negative and positive char carriers. Runs were also conducted with grains having additional fractional charges ranging between 0 and 1. By adding fractional charges of one sign, the model created grain populations in which all grains had excess charges the same sign, giving the cloud an overall net charge. This simulates clouds subjected to ionizing radiation (e. protoplanetary debris disk around a protosun), or any other process of charge biasing in a grain population (e.g., volcanic plumes). In another run series, random fractional charges of either sign were added to the grains so th some grains had a slight net positive charge while others had a slight net negative charge. This simulates triboelectrically-charged grain populations in which acquisition of an electron by one surface is at the expense creating a hole elsewhere. This dual sign charging was applied in two ways: in one case the cloud remained neutral by ensuring that all grain excess charges added to zero; in the other case, the cloud was permitted slight net char by not imposing a charge-balance condition. Additional information is contained in the original.
The properties of electrostatic ion-cyclotron waves excited in a single-ended cesium Q machine with a nonuniform magnetic field are described. The electrostatic ion-cyclotron waves are generated in the usual manner by drawing an electron current to a small exciter disk immersed in the plasma column. The parallel and perpendicular (to B) wavelengths and phase velocities are determined by mapping out two-dimensional wave phase contours. The wave frequency f depends on the location of the exciter disk in the nonuniform magnetic field, and propagating waves are only observed in the region where f is approximately greater than fci, where fci is the local ion-cyclotron frequency. The parallel phase velocity is in the direction of the electron drift. From measurements of the plasma properties along the axis, it is inferred that the electron drift velocity is not uniform along the entire current channel. The evidence suggests that the waves begin being excited at that axial position where the critical drift velocity is first exceeded, consistent with a current-driven excitation mechanism.
Operation of high impact rate particle injector for electrostatic accelerator to simulate long-term micrometeoroid bombardment
Shadow molecular dynamics provide an efficient and stable atomistic simulation framework for flexible charge models with long-range electrostatic interactions. Shadow molecular dynamics simulations are driven by approximate “shadow” Born–Oppenheimer potentials for which the exact charges and forces are directly accessible without relying on costly (and approximate) iterative solvers. While previous implementations have been limited to atomic monopole charge distributions, we extend this approach to flexible multipole models. We derive detailed expressions for the shadow energy functions, potentials, and force terms, explicitly incorporating monopole–monopole, dipole–monopole, and dipole–dipole interactions. In our formulation, both atomic monopoles and atomic dipoles are treated as extended dynamical variables alongside the propagation of the nuclear degrees of freedom. We demonstrate that introducing the additional dipole degrees of freedom preserves the stability and accuracy previously seen in monopole-only shadow molecular dynamics simulations. In addition, we present a shadow molecular dynamics scheme where the monopole charges are held fixed while the dipoles remain flexible. Our extended shadow dynamics provide a framework for stable, computationally efficient, and versatile molecular dynamics simulations involving long-range interactions between flexible multipoles. This is of particular current interest in combination with machine-learned interatomic potentials, including long-range electrostatic interactions.