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DOE OSTI2023

Efficient 3D Molecular Design with an E(3) Invariant Transformer VAE

This work introduces a three-dimensional (3D) invariant graph-to-string transformer variational autoencoders (VAE) (Vagrant) for generating molecules with accurate density functional theory (DFT)-level properties. Vagrant learns to model the joint probability distribution of a 3D molecular structure and its properties by encoding molecular structures into a 3D-aware latent space. Directed navigation through this latent space implicitly optimizes the 3D structure of a molecule, and the latent embedding can be used to condition a generative transformer to predict the candidate structure as a one-dimensional (1D) sequence. Additionally, we introduce two novel sampling methods that exploit the latent characteristics of a VAE to improve performance. Here, we show that our method outperforms comparable 3D autoregressive and diffusion methods for predicting quantum chemical property values of novel molecules in terms of both sample quality and computational efficiency.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Comparison of 1D and 2D particle-in-cell simulations for DC magnetron sputtering discharges

One-dimensional and two-dimensional particle-in-cell (PIC)/Monte Carlo collision simulations are performed for a balanced direct current magnetron sputtering (DCMS) discharge. The plasma parameters obtained by both simulations above the target racetrack share similar features and magnitudes under similar discharge intensities, i.e., similar local discharge current densities. The commonly observed high-energy tail of electron energy distribution in DCMS discharges is reproduced in both simulations. Additionally, the accuracy and applicability of using onedimensional simulations to qualitatively investigate the characteristics of balanced DCMS discharge are confirmed. In terms of capturing the key physical mechanisms in DCMS, one-dimensional PIC simulation is an efficient method, which could largely alleviate the computational expense and preserve the physical fidelity of modeling results.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

NASA NTRS1974

The polarizability of diatomic helium

The calculation of the electric dipole polarizability tensor of the He 2 dimer is described, and the results are used in the computation of several dielectric and optical properties of helium gas, at both high (322 K) and low (4 K) temperatures. The properties considered are the second dielectric virial coefficient, the second Kerr virial coefficient, and the depolarization ratio of the integrated intensities for the Raman scattering experiments. The thesis consists of five parts: the polarizability and various properties are defined; the calculation of the polarizability in the long-range region in terms of a quantum mechanical multipole expansion is described; the calculation of the He2 polarizability in the overlap region via coupled Hartree-Fock perturbation theory is described; the calculation of the quantum pair distribution function for both the He-3 and He-4 isotopes at 4 K is discussed; and the calculated values of the properties of helium gas are given.

Fortune, P. J.↗

DOE OSTI2022

Controlling extrapolations of nuclear properties with feature selection

Predictions of nuclear properties far from measured data are inherently inaccurate because of uncertainties in our knowledge of nuclear forces and in our treatment of quantum many-body effects in strongly-interacting systems. While the model bias can be directly calculated when experimental data is available, only an estimate can be made in the absence of such measurements. Current approaches to compute the estimated bias quickly lose predictive power when their input variables are taken far from the training region, resulting in uncontrolled uncertainties in applications such as nucleosynthesis simulations. In this letter, we present a novel technique to identify the input variables of machine learning algorithms that can provide robust estimates of model bias. Our process is based on selecting input variables, or features, based on their probability distribution functions across the entire nuclear chart. We illustrate our approach on the problem of quantifying the model bias in nuclear binding energies calculated with Density Functional Theory (DFT). We prove that building model biases with only Z and N as features leads to highly unreliable extrapolations. Conversely, we show that proper feature selection can systematically improve theoretical predictions without increasing uncertainties.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2021

Quantifying Uncertainty in All-to-All Estimates of Space Object Conjunction Probabilities using U-Statistics

Predicting space object conjunctions is inherently probabilistic due to initial state and orbit model uncertainty. A commonly considered Monte Carlo estimator of the conjunction probability is the ’all-to-all’ estimator. Given independent random samples of the trajectories of both objects, the estimator is the percentage of all pairs of trajectories that result in a conjunction. Intuitively, the all-to-all estimator is the best possible estimator of the conjunction probability since it considers all pairs of Monte Carlo samples. However, its distribution is not available in closed-form, which limits its use in practice and makes this intuition difficult to make rigorous. In this paper, the all-to-all estimator is identified as a U-statistic, which implies that it has several favorable properties. Specifically, the estimator is the minimum variance unbiased estimator of the conjunction probability and is asymptotically Gaussian distributed. An approximate confidence interval for the conjunction probability is obtained from an estimate of the asymptotic Gaussian distribution. We show how to efficiently compute the confidence interval and demonstrate that the interval has the nominal coverage level. The confidence intervals are also seen to be narrower than those based on the commonly-used each-to-each estimator. Furthermore, the all-to-all estimator is shown to allow different Monte Carlo sample sizes, whereas the each-to-each estimator requires equal sample sizes.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2024

Spin-orbit correlations in the nucleon in the large- N c limit

We study the twist-3 spin-orbit correlations of quarks described by the nucleon matrix elements of the parity-odd rank-2 tensor QCD operator (the parity-odd partner of the QCD energy-momentum tensor). Our treatment is based on the effective dynamics emerging from the spontaneous breaking of chiral symmetry and the mean-field picture of the nucleon in the large- N c limit. The twist-3 QCD operators are converted to effective operators, in which the QCD interactions are replaced by spin-flavor-dependent chiral interactions of the quarks with the pion field. We compute the nucleon matrix elements of the twist-3 effective operators and discuss the role of the chiral interactions in the spin-orbit correlations. We derive the first-quantized representation in the mean-field picture and develop a quantum-mechanical interpretation. The chiral interactions give rise to new spin-orbit couplings and qualitatively change the correlations compared to the quark model picture. We also derive the twist-3 matrix elements in the topological soliton picture where the quarks are integrated out (skyrmion). The methods used here can be extended to other QCD operators describing higher-twist nucleon structure and generalized parton distributions. Published by the American Physical Society 2024

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

NASA NTRS2016

Li-Doped Ionic Liquid Electrolytes: From Bulk Phase to Interfacial Behavior

Ionic liquids have been proposed as candidate electrolytes for high-energy density, rechargeable batteries. We present an extensive computational analysis supported by experimental comparisons of the bulk and interfacial properties of a representative set of these electrolytes as a function of Li-salt doping. We begin by investigating the bulk electrolyte using quantum chemistry and ab initio molecular dynamics to elucidate the solvation structure of Li(+). MD simulations using the polarizable force field of Borodin and coworkers were then performed, from which we obtain an array of thermodynamic and transport properties. Excellent agreement is found with experiments for diffusion, ionic conductivity, and viscosity. Combining MD simulations with electronic structure computations, we computed the electrochemical window of the electrolytes across a range of Li(+)-doping levels and comment on the role of the liquid environment. Finally, we performed a suite of simulations of these Li-doped electrolytes at ideal electrified interfaces to evaluate the differential capacitance and the equilibrium Li(+) distribution in the double layer. The magnitude of differential capacitance is in good agreement with our experiments and exhibits the characteristic camel-shaped profile. In addition, the simulations reveal Li(+) to be highly localized to the second molecular layer of the double layer, which is supported by additional computations that find this layer to be a free energy minimum with respect to Li(+) translation.

molecular dynamics↗

DOE OSTI2022

Discovering causal structure with reproducing-kernel Hilbert space ε -machines

We merge computational mechanics’ definition of causal states (predictively equivalent histories) with reproducing-kernel Hilbert space (RKHS) representation inference. The result is a widely applicable method that infers causal structure directly from observations of a system’s behaviors whether they are over discrete or continuous events or time. A structural representation—a finite- or infinite-state kernel ϵ-machine—is extracted by a reduced-dimension transform that gives an efficient representation of causal states and their topology. In this way, the system dynamics are represented by a stochastic (ordinary or partial) differential equation that acts on causal states. We introduce an algorithm to estimate the associated evolution operator. Paralleling the Fokker–Planck equation, it efficiently evolves causal-state distributions and makes predictions in the original data space via an RKHS functional mapping. We demonstrate these techniques, together with their predictive abilities, on discrete-time, discrete-value infinite Markov-order processes generated by finite-state hidden Markov models with (i) finite or (ii) uncountably infinite causal states and (iii) continuous-time, continuous-value processes generated by thermally driven chaotic flows. The method robustly estimates causal structure in the presence of varying external and measurement noise levels and for very high-dimensional data.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2019

Fraunhofer diffraction of a Gaussian beam by a four-sector binary grating with a half period fringes shift between adjacent sectors

The study of Fraunhofer diffraction of a Gaussian laser beam by a computer-generated, binary four-sector grating (FSG) is presented. In the four equal angular sectors of the FSG, parts of a binary rectilinear grating are embedded in such a way that, two neighboring parts are shifted by a half spatial rectilinear grating period. Analytical expressions, describing the diffracted wave field amplitude and intensity distributions in the focal plane of a converging lens, are derived. The zeroth-diffraction-order beam is a Gaussian beam whose transverse cross section has a reduced Gaussian radius. The higher-diffraction-order beam transverse intensity profiles have four bright spots divided by dark crossed lines and a central dark, non-vortex core. They can be described as cosine-Laguerre–Gaussian Kummer beams. The numerically calculated diffraction patterns, as well as the interference patterns of the diffracted beam with a slightly inclined plane wave, are supported by experimentally obtained ones. In both investigations (theoretical and experimental) the interferograms confirm the non-vortex nature of the diffraction spots.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2024

Scalable measurement error mitigation via iterative bayesian unfolding

Measurement errors are a significant obstacle to achieving scalable quantum computation. To counteract systematic readout errors, researchers have developed postprocessing techniques known as measurement error mitigation methods. However, these methods face a tradeoff between scalability and returning nonnegative probabilities. In this paper, we present a solution to overcome this challenge. Our approach focuses on iterative Bayesian unfolding, a standard mitigation technique used in high-energy physics experiments, and implements it in a scalable way. We demonstrate our method on experimental Greenberger-Horne-Zeilinger state preparation on up to 127 qubits and on the Bernstein-Vazirani algorithm on up to 26 qubits. Compared to state-of-the-art methods (such as M3), our implementation guarantees valid probability distributions, returns comparable or better-mitigated results, and does so without a noticeable time and memory overhead. Published by the American Physical Society 2024

Pokharel, Bibek (ORCID:0000000342841353)↗

DOE OSTI2020

Mass agnostic jet taggers

Searching for new physics in large data sets needs a balance between two competing effects—signal identification vs background distortion. In this work, we perform a systematic study of both single variable and multivariate jet tagging methods that aim for this balance. The methods preserve the shape of the background distribution by either augmenting the training procedure or the data itself. Multiple quantitative metrics to compare the methods are considered, for tagging 2-, 3-, or 4-prong jets from the QCD background. This is the first study to show that the data augmentation techniques of Planing and PCA based scaling deliver similar performance as the augmented training techniques of Adversarial NN and uBoost, but are both easier to implement and computationally cheaper.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2023

Quantum Monte Carlo calculations in configuration space with three-nucleon forces

Neutron matter, through its connection to neutron stars as well as systems like cold atom gases, is one of the most interesting yet computationally accessible systems in nuclear physics. The Configuration-Interaction Monte Carlo (CIMC) method is a stochastic many-body technique allowing to tackle strongly coupled systems. In contrast to other Quantum Monte Carlo methods employed in nuclear physics, the CIMC method can be formulated directly in momentum space allowing for an efficient use of non-local interactions. In this work we extend CIMC method to include three-nucleon interactions through the normal-ordered two-body approximation. Here, we present results for the equation of state of neutron matter in line with other many-body calculations that employ low resolution chiral interactions, and provide predictions for the momentum distribution and the static structure factor.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2022

Quancorde: Boosting fidelity with Quantum Canary Ordered Diverse Ensembles

On today's noisy imperfect quantum devices, execution fidelity tends to collapse dramatically for most applications beyond a handful of qubits. It is therefore imperative to employ novel techniques that can boost quantum fidelity in new ways. Here, this paper aims to boost quantum fidelity with Clifford canary circuits by proposing Quancorde: Quantum Canary Ordered Diverse Ensembles, a fundamentally newapproach to identifying the correct outcomes of extremely low-fidelity quantum applications. It is based on the key idea of diversity in quantum devices - variations in noise sources, make each (portion of a) device unique, and therefore, their impact on an application's fidelity, also unique. Quancorde utilizes Clifford canary circuits (which are classically simulable, but also resemble the target application structure and thus suffer similar structural noise impact) to order a diverse ensemble of devices or qubits/mappings approximately along the direction of increasing fidelity of the target application. Quancorde then estimates the correlation of the ensemble-wide probabilities of each output string of the application, with the canary ensemble ordering, and uses this correlation to weight the application's noisy probability distribution. The correct application outcomes are expected to have higher correlation with the canary ensemble order, and thus their probabilities are boosted in this process. Doing so, Quancorde improves the fidelity of evaluated quantum applications by a mean of 8.9x/4.2x (wrt. different baselines) and up to a maximum of 34x.

clifford↗

DOE OSTI2025

Phonon second harmonic generation in NaBr studied by inelastic neutron scattering and computer simulation

The phenomenon of second harmonic generation (SHG) was found for phonons in anharmonic NaBr by inelastic neutron scattering. The temperature dependence of this phonon SHG was measured from 300 K to 650 K. At 300 K the second harmonic (SH) is seen as a high-energy branch around 33 meV, nearly independent of $\overrightarrow{Q}$. The temperature effective potential (TDEP) method and classical molecular dynamics (MD) simulation with machine learning interatomic potential were able to reproduce the SH, and showed that SHG occurs with the flat transverse optical (TO) phonon branch. A classical model of a nonlinear medium explains the intensity and lifetime of the SH, compared to those of the TO modes. Also successful was a quantum model based on the Heisenberg-Langevin equation for interacting phonons coupled to a thermal bath, which also predicts a spectral distribution of the SH. In conclusion, the measured temperature dependence of the intensity of the second harmonic showed that it follows the Planck distribution of a one-phonon quasiparticle, and not two TO phonons.

36 MATERIALS SCIENCE↗

DOE OSTI2022

GenASiS Basics: Object-oriented utilitarian functionality for large-scale physics simulations (Version 4)

GenASiS Basics provides modern Fortran classes furnishing extensible object-oriented utilitarian functionality for large-scale physics simulations on distributed memory supercomputers. This functionality includes physical units and constants; display to the screen or standard output device; message passing; I/O to disk; and runtime parameter management and usage statistics. Herein, this revision—Version 4 of Basics—includes a name change and additions to functionality, including the facilitation of direct communication between GPUs.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2022

A second-order distributed memory parallel fast sweeping method for the Eikonal equation

The Eikonal equation is used to calculate wave propagation and distance fields, and due to its complexity requires numerical treatment for its solution. In this work, we present a second-order distributed memory parallel fast sweeping method. The second-order solution switches on a two-point stencil when two upwind points are available, and reverts to first-order otherwise. In all examples, the second-order method improves the solution over the first-order, allowing for significant savings in memory while achieving the same accuracy. Parallelization over distributed memory saw good weak scaling with optimal convergence. The computational time for second-order was approximately 2.5 times slower than first-order, where the largest amount of mesh points ran on 144 cores (512 GB) was ≈20 billion. The savings in memory from the second-order method combined with the distributed memory algorithm result in the ability to solve problems much larger than are possible with the serial first-order method.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

Investigating Wireless Quantum Key Distribution for Advanced Reactor Communications

Remote operation of small modular reactor (SMR) facilities is an appealing prospect for streamlined operation costs, safety concerns, and user convenience. The ability to guarantee security of communication channels between offsite users and nuclear reactor facilities is critical to enabling remote operations, given the sensitive nature of reactor state data. This report proposes a configuration implementing quantum key distribution (QKD) and advanced encryption standard (AES) protocols to establish secure reactor data feeds. The model outlined, which utilizes the Argonne-developed SeQUeNCe software package and custom MATLAB code, determines free-space channel losses for microwave-band communications in a variety of weather conditions and simulates QKD via ground-satellite links. This report demonstrates the feasibility of repeatable, real-time key generation and distribution for this configuration towards enabling secure wireless reactor communications.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2022

Spin relaxation rate for heavy quarks in weakly coupled QCD plasma

We compute the relaxation rate of the spin density of heavy quarks in a perturbative QCD plasma to leading-log order in the coupling constant g. The spin relaxation rate Γ s in spin hydrodynamics is shown to be Γ s ~ g 4 log(1/g)T(T/M) 2 in the heavy-quark limit T/M $\ll$ 1, which is smaller than the relaxation rate of other non- hydrodynamic modes by additional powers of T/M. We demonstrate three different methods to evaluate the spin relaxation rate: 1) the Green-Kubo formula in the spin hydrodynamic regime, 2) the spin density correlation function in the strict hydrodynamic limit, and 3) quantum kinetic theory of the spin distribution function in momentum space. We highlight the interesting differences between these methods, while they are ultimately connected to each other by the underlying Ward-Takahashi identity for the non-conserved spin density.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗