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Results for “Polarizability operator”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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30 records · Page 2

All sources NASA NTRS DOE OSTI

DOE OSTI2025

Tunable n- and p-Type Behavior of Ruddlesden–Popper and Dion–Jacobson 2D Perovskites

Interfacial band alignment in two-dimensional (2D)/three-dimensional (3D) perovskite heterostructures is important for maximizing separation, extraction, and collection of charge carriers, which, in turn, stabilize the perovskite layer during solar cell operation. Despite the wide use of 2D spacer cations for stabilizing perovskite solar cells, spacer cation exchange across the 2D/3D interface induces structural transformation and degradation in performance and stability. We have now examined the electrochemical and photoelectrochemical behavior of 2D Ruddlesden–Popper (RP) and Dion–Jacobson (DJ) perovskites to assess the impact of binding configuration on the interfacial charge separation and their operational stability. In-situ open circuit potential (OCP) responses of 2D perovskites under light illumination, paired with redox couples (ferrocene/ferrocenium), reveal different semiconducting polarizabilities of BA-RP (n-type) and BDA-DJ (p-type). The tunability of the Fermi energy level in 2D perovskites discussed in this study offers insight into the design of 2D/3D interfaces for optimizing charge transfer and enhancing charge neutrality and stability.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

E(n)-Equivariant cartesian tensor message passing interatomic potential

Machine learning potential (MLP) has been a popular topic in recent years for its capability to replace expensive first-principles calculations in some large systems. Meanwhile, message passing networks have gained significant attention due to their remarkable accuracy, and a wave of message passing networks based on Cartesian coordinates has emerged. However, the information of the node in these models is usually limited to scalars, and vectors. In this work, we propose High-order Tensor message Passing interatomic Potential (HotPP), an E(n) equivariant message passing neural network that extends the node embedding and message to an arbitrary order tensor. By performing some basic equivariant operations, high order tensors can be coupled very simply and thus the model can make direct predictions of high-order tensors such as dipole moments and polarizabilities without any modifications. The tests in several datasets show that HotPP not only achieves high accuracy in predicting target properties, but also successfully performs tasks such as calculating phonon spectra, infrared spectra, and Raman spectra, demonstrating its potential as a tool for future research.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Excited-state electronic structure of molecules using many-body Green’s functions: Quasiparticles and electron–hole excitations with VOTCA-XTP

Here, we present the open-source VOTCA-XTP software for the calculation of the excited-state electronic structure of molecules using many-body Green’s function theory in the GW approximation with the Bethe–Salpeter equation (BSE). This work provides a summary of the underlying theory and discusses the details of its implementation based on Gaussian orbitals, including resolution-of-identity techniques and different approaches to the frequency integration of the self-energy or acceleration by offloading compute-intensive matrix operations using graphics processing units in a hybrid OpenMP/Cuda scheme. A distinctive feature of VOTCA–XTP is the capability to couple the calculation of electronic excitations to a classical polarizable environment on an atomistic level in a coupled quantum- and molecular-mechanics (QM/MM) scheme, where a complex morphology can be imported from Molecular Dynamics simulations. The capabilities and limitations of the GW–BSE implementation are illustrated with two examples. First, we study the dependence of optically active electron–hole excitations in a series of diketopyrrolopyrrole-based oligomers on molecular-architecture modifications and the number of repeat units. Second, we use the GW–BSE/MM setup to investigate the effect of polarization on localized and intermolecular charge-transfer excited states in morphologies of low-donor content rubrene–fullerene mixtures. These showcases demonstrate that our implementation currently allows us to treat systems with up to 2500 basis functions on regular shared-memory workstations, providing accurate descriptions of quasiparticle and coupled electron–hole excited states of various characters on an equal footing.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2022

Mutually guided light and particle beam propagation

The polarizability of atoms and molecules gives rise to optical forces that trap particles and a refractive index that guides light beams, potentially leading to a self-guided laser and particle beam propagation. In this paper, the mutual interactions between an expanding particle beam and a diffracting light beam are investigated using an axisymmetric particle-light coupled simulation. The nonlinear coupling between particles and photons is dependent on the particle beam radius, particle density, particle velocity and temperature, polarizability, light beam waist, light frequency (with respect to the resonance frequency), and light intensity. The computational results show that the maximum propagation distance is achieved when the waveguiding effect is optimized to single-mode operation. The application of the coupled beam propagation as a space propulsion system is discussed.

42 ENGINEERING↗

DOE OSTI2022

Gaussian process for calibration and control of GlueX Central Drift Chamber

We have developed and implemented a machine learning based system to calibrate and control the GlueX Central Drift Chamber at Jefferson Lab, VA, in near real-time. The system monitors environmental and experimental conditions during data taking and uses those as inputs to a Gaussian process (GP) with learned prior. The GP predicts calibration constants in order to recommend a high voltage (HV) setting for the detector that maintains consistent detector performance (gain and resolution) throughout data taking. This approach is in stark contrast to traditional detector operations in which the detector operates at fixed HV and its calibration parameters vary quite considerably with time. Additionally, the ML based system utilizes uncertainty quantification to correct the recommended control parameters when appropriate. We will present results from the ML system autonomously during the Charged Pion Polarizability (CPP) experiment conducted in Hall D at Jefferson Lab.

McSpadden, Helen↗

DOE OSTI2022

Gaussian process for calibration and control of GlueX Central Drift Chamber

McSpadden, Helen↗

DOE OSTI2025

A Universal Model of Cation Effects in Electrocatalysis

Electrolyte cations are conventionally viewed as inert spectators in electrocatalysis. However, a wealth of observations show that catalytic rates are often highly sensitive to cation identity. Despite their prevalence, these cation effects have resisted a unified mechanistic explanation, with different physical phenomena implicated across reaction chemistries, catalyst compositions, and choice of solvent. In this perspective, we describe a general framework for understanding cation effects in electrocatalysis based on electrostatics. We argue that cations influence reaction rates by modifying the strength of the electric field present at the catalyst surface, which alters the energetics of adsorbed intermediates and transition states according to their dipole moments and polarizabilities. The magnitude of this field depends on how cations arrange at the electrode surface, controlled by their size, shape, solvation, and packing efficiency. Cations that can arrange more densely result in a steeper potential drop at the electrode surface and consequently a stronger electric field. Our model further identifies two criteria for observing cation effects: (1) the operating potential must be negative of the electrode’s potential of zero total charge, ensuring that cations accumulate at the interface, and (2) the energetics of the kinetically relevant elementary step must be field sensitive. This framework reconciles previously inconsistent trends, including why cation effects appear only for some catalysts, why reaction selectivity is sensitive to cation identity, and why activity can increase with cation size on certain metals but decrease on others. Supported by kinetic measurements, spectroscopy, and atomistic simulations, the model provides both conceptual value for building intuition about catalysis at charged interfaces and predictive value for anticipating trends for new reactions, catalysts, and electrolytes. We conclude by highlighting the importance of electric fields across electrochemical, thermochemical, and biological catalysis and propose that considering the electrostatic environment around active sites offers new opportunities for improving activity and selectivity.

cation effects↗

NASA NTRS1979

On nuclear spin statistics in rotational transition intensities in tetrahedral AB4 molecules

A general expression is derived for the integrated intensity of rotational transitions in the vibronic ground state of tetrahedral molecules, taking into account the nuclear spin statistics. It is shown that the ratio of this expression to previously published spin-free integrated intensities depends only on the tensor character N of the operator driving the transition, the appropriate rotational quantum numbers J and J', and the nuclear spin of the identical nuclei. Tables are given for N = 3, 4 and J no more than 50, which enable the calculation of integrated intensities for octopole and hexadecapole collision-induced dipole-moment transitions, centrifugal-distortion-induced dipole-moment transitions, and centrifugal-distortion-induced anisotropic-polarizability-tensor Raman transitions.

Rosenberg, A.↗

NASA NTRS1974

Electric Field Induced Spectra of H sub 2 and D sub 2

The frequencies of four Q-branch lines of H2 and five Q-branch lines of D2 were measured as a function of density, and their shifts were observed to be in the linear region. The individual slopes and extrapolated zero density frequency of each line was determined. Hydrogen was measured for polarizability using the integrated intensity of the Q1(0) and S1(1), H2 absorption line. A highly automated technique for determining the response function of the spectrometer using digitally recorded data is presented. For the Q1(0) and Q1(1) lines of H2 the halfwidths were measured as a function of electric field intensity at constant pressure, and again at several densities and compared to previously measured widths. Technical and operational details of equipment built for this experiment, and for the five-meter Littrow spectrometer used, are described. Modifications of the spectrometer optics to accept the Stark cell are discussed.

Boyd, William Joseph↗

DOE OSTI2022

Epitaxial Sc x Al 1- x N on GaN exhibits attractive high-K dielectric properties

Epitaxial Sc x Al 1- x N thin films of ~100 nm thickness grown on metal polar GaN substrates are found to exhibit significantly enhanced relative dielectric permittivity (ε r ) values relative to AlN. ε r values of ~17–21 for Sc mole fractions of 17%–25% ( x = 0.17–0.25) measured electrically by capacitance–voltage measurements indicate that Sc x Al 1- x N has the largest relative dielectric permittivity of any existing nitride material. Since epitaxial Sc x Al 1- x N layers deposited on GaN also exhibit large polarization discontinuity, the heterojunction can exploit the in situ high-K dielectric property to extend transistor operation for power electronics and high-speed microwave applications.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Computing the solubility of argon and xenon in molten sodium chloride and potassium chloride salts

Molten salt reactors (MSRs) offer significant advancements in nuclear reactor safety and efficiency by operating at higher temperatures and lower pressures compared to traditional reactors. A critical aspect of MSR operation involves understanding the solubility of fission byproducts, particularly noble gases, in the molten salts used. This study employs molecular dynamics (MD) simulations to compute Henry’s law constants and enthalpies of solvation for argon and xenon in molten sodium chloride (NaCl) and potassium chloride (KCl). We developed a new pairwise potential for the noble gas and salt interactions based on first principles calculations. We then used this potential to calculate Henry’s law constants of the two gases in the molten salts, which were modeled using both a rigid ion model (RIM) and a polarizable ion model (PIM). The solubility calculations, performed using the Widom insertion method, show qualitative agreement with limited experimental data, highlighting the temperature dependence and greater solubility of both gases in KCl compared to NaCl. Additionally, free volume analysis elucidated the role of available space within the molten salts in governing solubility trends. Our findings suggest that PIM trajectories provide more reliable predictions for noble gas solubility than RIM due to their accurate density representation. Furthermore, these results enhance understanding of gas solubility in MSR environments, and the methods can be readily extended to other systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

Deep Learning Coordinate-Free Quantum Chemistry

Computing quantum chemical properties of small molecules and polymers can provide insights valuable to physicists, chemists and biologists when designing new materials, catalysts, biological probes and drugs. Deep learning can compute quantum chemical properties accurately in a fraction of the time required by commonly used methods such as density functional theory (DFT). However, many of these deep learning architectures require energy minimized molecular geometries as input, which is also computationally expensive, and decreasing the reproducibility and throughput of these methods. In this study, we demonstrate that accurate quantum chemical computations can be performed without optimized geometries by operating in the coordinate-free domain using deep learning on graph encodings. Furthermore, we also find that the choice of graph-encoding architecture substantially affects the performance of these methods. The Wave architecture outperforms graph convolution architectures, particularly on complex molecules. Furthermore, the structures of these graph encoding architectures provide an opportunity to probe an important, outstanding question in quantum mechanics: What types of quantum chemical properties can be represented by local-variable models? We find that Wave, a local-variable model, is more accurately calculates quantum chemical properties. Graph convolutional architectures require global variables, and are not as effective as as Wave. We anticipate that coordinate-free, deep-learning models of quantum chemistry will become valuable tools in chemistry and biology, enabling researchers to rapidly screen chemical databases or identify new molecules using automated, de-novo design algorithms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗