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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Cluster expansion by transfer learning for phase stability predictions

Recent progress towards universal machine-learned interatomic potentials holds considerable promise for materials discovery. Yet the accuracy of these potentials for predicting phase stability may still be limited. In contrast, cluster expansions provide accurate phase stability predictions but are computationally demanding to parameterize from first principles, especially for structures of low dimension or with a large number of components, such as interfaces or multimetal catalysts. We overcome this trade-off via transfer learning. Using Bayesian inference, we incorporate prior statistical knowledge from machine-learned and physics-based potentials, enabling us to sample the most informative configurations and to efficiently fit first-principles cluster expansions. Furthermore, this algorithm is tested on Pt:Ni, showing robust convergence of the mixing energies as a function of sample size with reduced statistical fluctuations.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Understanding the Physics Representation of Deep Learning Models in Environmental Applications

Deep learning (DL) models have been popular in earth and environmental modeling and analysis, which exhibit huge potential in capturing and reconstructing the non-linearity of relevant environmental processes. They are extensively used as analytical tools or emulators for multiple domains (atmosphere, land surface, ocean, and biogeochemistry). Despite their success, their internal working mechanism remains largely unknown. Such a lack of knowledge hinders the identification of physically consistent models that are fully adaptive to non-stationary climate, as well as the development of physics-informed machine learning such as physics-informed neural network (PINN). To establish preliminary knowledge and framework of such physics representation evaluation, this project focuses on an improved understanding of DL models in the environmental applications. DL models are increasingly applied to environmental modeling and prediction. However, they have been evaluated mostly from a performance perspective, and there is a gap in understanding how they represent the known physics internally. Such knowledge is especially critical when applying DL models under climate change conditions, where new inputs are likely outside the ranges of the training datasets. In this project, we reveal how the known physical processes are represented within DL models from both statistical and mechanistic perspectives. Leveraging the traditional model evaluations that focus more on the accuracies of predictions, we establish a framework that examines both the accuracy and physics representation of DL models. This analysis framework can identify DL models that make the correct predictions based on correct physics, thus enhancing the existing explainable artificial intelligence (explainable-AI) portfolio. It lays a foundation for developing novel metrics to evaluate the emerging DL models in environmental applications. This knowledge also informs the development of physics-informed DL models by revealing the direct connections between the known physical processes and specific model components or structures.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Classification without labels: learning from mixed samples in high energy physics

Modern machine learning techniques can be used to construct powerful models for difficult collider physics problems. In many applications, however, these models are trained on imperfect simulations due to a lack of truth-level information in the data, which risks the model learning artifacts of the simulation. In this paper, we introduce the paradigm of classification without labels (CWoLa) in which a classifier is trained to distinguish statistical mixtures of classes, which are common in collider physics. Crucially, neither individual labels nor class proportions are required, yet we prove that the optimal classifier in the CWoLa paradigm is also the optimal classifier in the traditional fully-supervised case where all label information is available. After demonstrating the power of this method in an analytical toy example, we consider a realistic benchmark for collider physics: distinguishing quark- versus gluon-initiated jets using mixed quark/gluon training samples. More generally, CWoLa can be applied to any classification problem where labels or class proportions are unknown or simulations are unreliable, but statistical mixtures of the classes are available.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Piecewise interaction picture density matrix quantum Monte Carlo

The density matrix quantum Monte Carlo (DMQMC) set of methods stochastically samples the exact N-body density matrix for interacting electrons at finite temperature. We introduce a simple modification to the interaction picture DMQMC (IP-DMQMC) method that overcomes the limitation of only sampling one inverse temperature point at a time, instead allowing for the sampling of a temperature range within a single calculation, thereby reducing the computational cost. At the target inverse temperature, instead of ending the simulation, we incorporate a change of picture away from the interaction picture. The resulting equations of motion have piecewise functions and use the interaction picture in the first phase of a simulation, followed by the application of the Bloch equation once the target inverse temperature is reached. We find that the performance of this method is similar to or better than the DMQMC and IP-DMQMC algorithms in a variety of molecular test systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Systematic and statistical uncertainties in simulated r -process abundances due to uncertain nuclear masses

Unknown nuclear masses are a major source of nuclear physics uncertainty for r -process nucleosynthesis calculations. Here we examine the systematic and statistical uncertainties that arise in r -process abundance predictions due to uncertainties in the masses of nuclear species on the neutron-rich side of stability. There is a long history of examining systematic uncertainties by the application of a variety of different mass models to r-process calculations. Here we expand upon such efforts by examining six DFT mass models, where we capture the full impact of each mass model by updating the other nuclear properties — including neutron capture rates, β-decay lifetimes, and β-delayed neutron emission probabilities — that depend on the masses. Unlike systematic effects, statistical uncertainties in the r-process pattern have just begun to be explored. Here we apply a global Monte Carlo approach, starting from the latest FRDM masses and considering random mass variations within the FRDM r ms error. Here, we find in each approach that uncertain nuclear masses produce dramatic uncertainties in calculated r -process yields, which can be reduced in upcoming experimental campaigns.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2018

Evidence for Majorana bound states in an iron-based superconductor

The search for Majorana bound state (MBS) has recently emerged as one of most active research areas in condensed matter physics, due to its non-Abelian statistics which can be used for robust quantum computation. A highly soughtafter platform for MBS is two-dimensional topological superconductors, where MBS is predicted to exist as a zero-energy mode in the core of a vortex. A clear observation of MBS, however, is often hindered by the presence of additional low-energy bound states inside the vortex core. By using scanning tunneling microscope on the newly discovered superconducting topological surface state of iron-based superconductor FeTe 1-x Se x (x = 0.45, T c = 14.5 K), we clearly observe a pristine MBS inside a vortex core, well separated from non-topological bound states which are pushed away from zero energy due to the high ratio between the superconducting gap and the Fermi energy in this material. This observation offers a new, robust platform for realizing and manipulating Majorana bound states at a relatively high temperature.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2014

General displaced SU(1, 1) number states: Revisited

The most general displaced number states, based on the bosonic and an irreducible representation of the Lie algebra symmetry of su(1, 1) and associated with the Calogero-Sutherland model are introduced. Here, we utilize the Barut-Girardello displacement operator instead of the Klauder-Perelomov counterpart, to construct new kind of the displaced number states which can be classified in nonlinear coherent states regime, too, with special nonlinearity functions. They depend on two parameters, and can be converted into the well-known Barut-Girardello coherent and number states, respectively, depending on which of the parameters equal to zero. A discussion of the statistical properties of these states is included. Significant are their squeezing properties and anti-bunching effects which can be raised by increasing the energy quantum number. Depending on the particular choice of the parameters of the above scenario, we are able to determine the status of compliance with flexible statistics. Major parts of the issue is spent on something that these states, in fact, should be considered as new kind of photon-added coherent states, too. Which can be reproduced through an iterated action of a creation operator on new nonlinear Barut-Girardello coherent states. Where the latter carry, also, outstanding statistical features.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Many-Body Physics in the NISQ Era: Quantum Programming a Discrete Time Crystal

Recent progress in the realm of noisy intermediate-scale quantum (NISQ) devices represents an exciting opportunity for many-body physics by introducing new laboratory platforms with unprecedented control and measurement capabilities. We explore the implications of NISQ platforms for many-body physics in a practical sense: we ask which physical phenomena, in the domain of quantum statistical mechanics, they may realize more readily than traditional experimental platforms. While a universal quantum computer can simulate any system, the eponymous noise inherent to NISQ devices practically favors certain simulation tasks over others in the near term. As a particularly well-suited target, we identify discrete time crystals (DTCs), novel nonequilibrium states of matter that break time translation symmetry. These can only be realized in the intrinsically out-of-equilibrium setting of periodically driven quantum systems stabilized by disorder-induced many-body localization. While promising precursors of the DTC have been observed across a variety of experimental platforms—ranging from trapped ions to nitrogen-vacancy centers to NMR crystals—none have all the necessary ingredients for realizing a fully fledged incarnation of this phase, and for detecting its signature long-range spatiotemporal order. We show that a new generation of quantum simulators can be programmed to realize the DTC phase and to experimentally detect its dynamical properties, a task requiring extensive capabilities for programmability, initialization, and readout. Specifically, the architecture of Google’s Sycamore processor is a remarkably close match for the task at hand. We also discuss the effects of environmental decoherence, and how they can be distinguished from ‘internal’ decoherence coming from closed-system thermalization dynamics. Already with existing technology and noise levels, we find that DTC spatiotemporal order would be observable over hundreds of periods, with parametric improvements to come as the hardware advances.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS1975

Some statistical analysis techniques for the effect of systematic instrument errors on the results of measurements in a space experiment

Mathematical models of certain systematic telemetry signal errors arising during the passage of the signal through the recording and transmitting blocks of the physical instruments aboard the spacecraft are classified and constructed. Statistical analysis methods are used to determine the background function and relative sensitivity coefficients of the channels of a charged particle detector. Results are presented. A block diagram for a method taking into account the effect of distortions is given.

Kondakov, S. P.↗
DOE OSTI2018

A two-phase mixing layer between parallel gas and liquid streams: multiphase turbulence statistics and influence of interfacial instability

The two-phase mixing layer formed between parallel gas and liquid streams is an important fundamental problem in turbulent multiphase flows. The problem is relevant to many industrial applications and natural phenomena, such as air-blast atomizers in fuel injection systems and breaking waves in the ocean. The velocity difference between the gas and liquid streams triggers an interfacial instability which can be convective or absolute depending on the stream properties and injection parameters. In the present study, a direct numerical simulation of a two-phase gas–liquid mixing layer that lie in the absolute instability regime is conducted. A dominant frequency is observed in the simulation and the numerical result agrees well with the prediction from viscous stability theory. As the interfacial wave plays a critical role in turbulence transition and development, the temporal evolution of turbulent fluctuations (such as the enstrophy) also exhibits a similar frequency. To investigate the statistical response of the multiphase turbulence flow, the simulation has been run for a long physical time so that time-averaging can be performed to yield the statistically converged results for Reynolds stresses and the turbulent kinetic energy (TKE) budget. An extensive mesh refinement study using from 8 million to about 4 billions cells has been performed. The turbulent dissipation is shown to be highly demanding on mesh resolution compared with other terms in TKE budget. The results obtained with the finest mesh are shown to be close to converged results of turbulent dissipation which allow us to obtain estimations of the Kolmogorov and Hinze scales. The estimated Kolmogorov scale is found to be similar to the cell size of the finest mesh used here. The computed Hinze scale is significantly larger than the size of droplets observed and does not seem to be a relevant length scale to describe the smallest size of droplets formed in atomization.

Mechanics↗
DOE OSTI2021

Physics‐based iterative reconstruction for dual‐source and flying focal spot computed tomography

Purpose For single‐source helical Computed Tomography (CT), both Filtered‐Back Projection (FBP) and statistical iterative reconstruction have been investigated. However, for dual‐source CT with flying focal spot (DS‐FFS CT), a statistical iterative reconstruction that accurately models the scanner geometry and acquisition physics remains unknown to researchers. Therefore, our purpose is to present a novel physics‐based iterative reconstruction method for DS‐FFS CT and assess its image quality. Methods Our algorithm uses precise physics models to reconstruct from the native cone‐beam geometry and interleaved dual‐source helical trajectory of a DS‐FFS CT. To do so, we construct a noise physics model to represent data acquisition noise and a prior image model to represent image noise and texture. In addition, we design forward system models to compute the locations of deflected focal spots, the dimension, and sensitivity of voxels and detector units, as well as the length of intersection between x‐rays and voxels. The forward system models further represent the coordinated movement between the dual sources by computing their x‐ray coverage gaps and overlaps at an arbitrary helical pitch. With the above models, we reconstruct images by an advanced Consensus Equilibrium (CE) numerical method to compute the maximum a posteriori estimate to a joint optimization problem that simultaneously fits all models. Results We compared our reconstruction with Siemens ADMIRE, which is the clinical standard hybrid iterative reconstruction (IR) method for DS‐FFS CT, in terms of spatial resolution, noise profile, and image artifacts through both phantoms and clinical scan datasets. Experiments show that our reconstruction has a higher spatial resolution, with a Task‐Based Modulation Transfer Function (MTF task ) consistently higher than the clinical standard hybrid IR. In addition, our reconstruction shows a reduced magnitude of image undersampling artifacts than the clinical standard. Conclusions By modeling a precise geometry and avoiding data rebinning or interpolation, our physics‐based reconstruction achieves a higher spatial resolution and fewer image artifacts with smaller magnitude than the clinical standard hybrid IR.

Wang, Xiao↗
DOE OSTI2022

HL-LHC Analysis With ROOT: ROOT Project Input to the HL-LHC Computing Review (Stage 2)

ROOT is high energy physics' software for storing and mining data in a statistically sound way, to publish results with scientific graphics. It is evolving since 25 years, now providing the storage format for more than one exabyte of data; virtually all high energy physics experiments use ROOT. With another significant increase in the amount of data to be handled scheduled to arrive in 2027, ROOT is preparing for a massive upgrade of its core ingredients. As part of a review of crucial software for high energy physics, the ROOT team has documented its R&D plans for the coming years.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2017

Computational and analytical comparison of flux discretizations for the semiconductor device equations beyond Boltzmann statistics

We compare three thermodynamically consistent numerical fluxes known in the literature, appearing in a Voronoï finite volume discretization of the van Roosbroeck system with general charge carrier statistics. Our discussion includes an extension of the Scharfetter–Gummel scheme to non-Boltzmann (e.g. Fermi–Dirac) statistics. It is based on the analytical solution of a two-point boundary value problem obtained by projecting the continuous differential equation onto the interval between neighboring collocation points. Hence, it serves as a reference flux. The exact solution of the boundary value problem can be approximated by computationally cheaper fluxes which modify certain physical quantities. One alternative scheme averages the nonlinear diffusion (caused by the non-Boltzmann nature of the problem), another one modifies the effective density of states. To study the differences between these three schemes, we analyze the Taylor expansions, derive an error estimate, visualize the flux error and show how the schemes perform for a carefully designed p-i-n benchmark simulation. We present strong evidence that the flux discretization based on averaging the nonlinear diffusion has an edge over the scheme based on modifying the effective density of states.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Energy considerations in the Community Atmosphere Model (CAM)

An error in the energy formulation in the Community Atmosphere Model (CAM) is identified and corrected. Ten year AMIP simulations are compared using the correct and incorrect energy formulations. Statistics of selected primary variables all indicate physically insignificant differences between the simulations, comparable to differences with simulations initialized with rounding sized perturbations. The two simulations are so similar mainly because of an inconsistency in the application of the incorrect energy formulation in the original CAM. CAM used the erroneous energy form to determine the states passed between the parameterizations, but used a form related to the correct formulation for the state passed from the parameterizations to the dynamical core. If the incorrect form is also used to determine the state passed to the dynamical core the simulations are significantly different. In addition, CAM uses the incorrect form for the global energy fixer, but that seems to be less important. The difference of the magnitude of the fixers using the correct and incorrect energy definitions is very small.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Assessing concurrent effects of climate change on hydropower supply, electricity demand, and greenhouse gas emissions in the Upper Yangtze River Basin of China

Hydropower importantly provides flexible low-carbon electricity, however, climate change will affect the hydropower system through altering hydrologic regimes while also affecting electricity demands for heating and cooling that hydropower resources serve. This study assesses the effect of climate change on hydropower and electricity demand in the Upper Yangtze River Basin (UYRB) in China on the regional net electric load and greenhouse gas (GHG) emissions. This is accomplished by using climate projections from five global climate models (GCMs) to simultaneously force (1) a physically-based hydrological model and a statistically-based hydropower model to estimate the future generating capacity of 21 large hydropower plants in the UYRB and (2) an empirical electricity demand model accounting for socioeconomic and climatic factors. Under climate change, the projected hydropower generation in the UYRB tends to increase in the 21st century but is far less than the increase in electricity demand, increasing the gap between demand and supply. Future increases in overall electricity demand are driven by GDP growth, but climate change will alter the distribution of the seasonal electricity demand. Climate warming decreases electricity demand for heating in winter and increases electricity demand for cooling in summer, but ultimately increases demand. Meanwhile, there is an increasing mismatch between electricity demand and hydropower supply associated with inter- and intra-annual variations, owing to the temporal climate change and increase in compound climate extremes (droughts and heatwaves). Finally, meeting the gap between supply and demand due to climate change is estimated to contribute 79.0–184.6 and 50.6–316.2 MMT CO 2e /yr of additional GHG emissions by the mid and end of 21st century, respectively.

13 HYDRO ENERGY↗
DOE OSTI2021

Statistical description of coalescing magnetic islands via magnetic reconnection

The physical picture of interacting magnetic islands provides a useful paradigm for certain plasma dynamics in a variety of physical environments, such as the solar corona, the heliosheath and the Earth's magnetosphere. In this work, we derive an island kinetic equation to describe the evolution of the island distribution function (in area and in flux of islands) subject to a collisional integral designed to account for the role of magnetic reconnection during island mergers. This equation is used to study the inverse transfer of magnetic energy through the coalescence of magnetic islands in two dimensions. We solve our island kinetic equation numerically for three different types of initial distribution: Dirac delta, Gaussian and power-law distributions. The time evolution of several key quantities is found to agree well with our analytical predictions: magnetic energy decays as $\tilde {t}^{-1}$ , the number of islands decreases as $\tilde {t}^{-1}$ and the averaged area of islands grows as $\tilde {t}$ , where $\tilde {t}$ is the time normalised to the characteristic reconnection time scale of islands. General properties of the distribution function and the magnetic energy spectrum are also studied. Finally, we discuss the underlying connection of our island-merger models to the (self-similar) decay of magnetohydrodynamic turbulence.

Physics↗
DOE OSTI2025

Spinon excitations and spin correlations in the one-dimensional quantum magnet β -VOSO 4 probed by Raman spectroscopy

Fractionalized excitations such as spinons and anyons have emerged as a central theme in condensed matter physics with broad implications for superconductivity, quantum statistics, and quantum computation. The nearly ideal one-dimensional S = 1/2 system β-VOSO 4 without long-range order down to 85 mK provides a promising platform to experimentally explore such fractionalized excitations. Here, we employ Raman spectroscopy to probe magnetic excitations and the evolution of spin correlations in β-VOSO 4 . Spinon signatures are found along the chain direction, evidenced by a broad, gapless scattering continuum at low temperatures. The temperature dependence of the spinon spectral weight aligns considerably with numerical density matrix renormalization group calculations. By comparing the experimental spinon spectral weight with calculated results and evaluating the associated quantum Fisher information (QFI) therefrom, we observe a steep increase in QFI upon cooling, indicating rapidly growing correlation lengths. Our study showcases QFI as a probe of spin correlations in quantum magnets.

Wulferding, Dirk [Sejong Univ., Seoul (Korea, Repu↗
DOE OSTI2014

Modeling the neutral hydrogen distribution in the post-reionization Universe: intensity mapping

We model the distribution of neutral hydrogen (HI) in the post-reionization era and investigate its detectability in 21 cm intensity mapping with future radio telescopes like the Square Kilometer array (SKA). We rely on high resolution hydrodynamical N-body simulations that have a state-of-the-art treatment of the low density photoionized gas in the inter-galactic medium (IGM). The HI is assigned a-posteriori to the gas particles following two different approaches: a halo-based method in which HI is assigned only to gas particles residing within dark matter halos; a particle-based method that assigns HI to all gas particles using a prescription based on the physical properties of the particles. The HI statistical properties are then compared to the observational properties of Damped Lyman-α Absorbers (DLAs) and of lower column density systems and reasonable good agreement is found for all the cases. Among the halo-based method, we further consider two different schemes that aim at reproducing the observed properties of DLAs by distributing HI inside halos: one of this results in a much higher bias for DLAs, in agreement with recent observations, which boosts the 21 cm power spectrum by a factor ∼ 4 with respect to the other recipe. Furthermore, we quantify the contribution of HI in the diffuse IGM to both Ω{sub HI} and the HI power spectrum finding to be subdominant in both cases. We compute the 21 cm power spectrum from the simulated HI distribution and calculate the expected signal for both SKA1-mid and SKA1-low configurations at 2.4 ≤ z ≤ 4. We find that SKA will be able to detect the 21 cm power spectrum, in the non-linear regime, up to k ∼ 1 h/Mpc for SKA1-mid and k ∼ 5 h/Mpc for SKA1-low with 100 hours of observations. We also investigate the perspective of imaging the HI distribution. Our findings indicate that SKA1-low could detect the most massive HI peaks with a signal to noise ratio (SNR) higher than 5 for an observation time of about 1000 hours at z = 4, for a synthesized beam width of 2'. Detection at redshifts z≥2.4 with SKA1-mid would instead require a much longer observation time to achieve a comparable SNR level.

79 ASTRONOMY AND ASTROPHYSICS↗