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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Using Machine Learning to Generate a GISS ModelE Calibrated Physics Ensemble (CPE)

A neural network (NN) surrogate of the NASA GISS ModelE atmosphere (version E3) is trained on a perturbed parameter ensemble (PPE) spanning 45 physics parameters and 36 outputs. The NN is leveraged in a Markov Chain Monte Carlo (MCMC) Bayesian parameter inference framework to generate a second posterior constrained ensemble coined a “calibrated physics ensemble,” or CPE. The CPE members are characterized by diverse parameter combinations and are, by definition, close to top-of-atmosphere radiative balance, and must broadly agree with numerous hydrologic, energy cycle and radiative forcing metrics simultaneously. Global observations of numerous cloud, environment, and radiation properties (provided by global satellite products) are crucial for CPE generation. The inference framework explicitly accounts for discrepancies (or biases) in satellite products during CPE generation. We demonstrate that product discrepancies strongly impact calibration of important model parameter settings (e.g., convective plume entrainment rates; fall speed for cloud ice). Structural improvements new to E3 are retained across CPE members (e.g., stratocumulus simulation). Notably, the framework improved the simulation of shallow cumulus and Amazon rainfall while not degrading radiation fields, an upgrade that neither default parameters nor Latin Hypercube parameter searching achieved. Analyses of the initial PPE suggested several parameters were unimportant for output variation. However, many “unimportant” parameters were needed for CPE generation, a result that brings to the forefront how parameter importance should be determined in PPEs. From the CPE, two diverse 45-dimensional parameter configurations are retained to generate radiatively-balanced, auto-tuned atmospheres that were used in two E3 submissions to CMIP6.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Phase behavior of continuous-space systems: A supervised machine learning approach

The phase behavior of complex fluids is a challenging problem for molecular simulations. Supervised machine learning (ML) methods have shown potential for identifying the phase boundaries of lattice models. In this work, we extend these ML methods to continuous-space systems. We propose a convolutional neural network model that utilizes grid-interpolated coordinates of molecules as input data of ML and optimizes the search for phase transitions with different filter sizes. We test the method for the phase diagram of two off-lattice models, namely, the Widom–Rowlinson model and a symmetric freely jointed polymer blend, for which results are available from standard molecular simulations techniques. The ML results show good agreement with results of previous simulation studies with the added advantage that there is no critical slowing down. We find that understanding intermediate structures near a phase transition and including them in the training set is important to obtain the phase boundary near the critical point. The method is quite general and easy to implement and could find wide application to study the phase behavior of complex fluids.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Impact of UV-induced ozone and low-energy Ar + -ion cleaning on the chemical structure of Cu(In,Ga)(S,Se) 2 absorber surfaces

Dry buffer layer deposition techniques for chalcopyrite (CIGSSe)-based thin-film solar cells lack the surface-cleaning characteristics of the commonly used CdS or Zn(O,S) wet-chemical bath deposition. Here, a UV-induced ozone and/or a low-energy Ar + -ion treatment could provide dry CIGSSe surface cleaning steps. To study the impact of these treatments, the chemical surface structure of a CIGSSe absorber is investigated. For this purpose, a set of surface-sensitive spectroscopic methods, i.e., laboratory-based x-ray photoelectron spectroscopy and x-ray-excited Auger electron spectroscopy, is combined with synchrotron-based soft x-ray emission spectroscopy. After treatment times as short as 15 s, the UV-induced ozone treatment decreases the amount of carbon adsorbates at the CIGSSe surface significantly, while the oxygen content increases. This is accompanied by the oxidation of all absorber surface elements, i.e., indium, selenium, sulfur, and copper. Short (60 s) low-energy Ar + -ion treatments, in contrast, primarily remove oxygen from the surface. Longer treatment times also lead to a removal of carbon, while extremely long treatment times can also lead to additional (likely metallic) Cu phases at the absorber surface as well.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

An extended Vlasov–Fokker–Planck approach for kinetic simulations of laser plasmas

Vlasov–Fokker–Planck simulation codes occupy an important niche in modeling laser-produced plasmas, since they are well suited to studying the effect of collisions on electron kinetic phenomena, especially energy transport. One of the most important elements of energy transport is the absorption of laser light by the plasma; however, simulating this in detail requires resolving oscillations of the laser light, whose characteristic timescale is orders of magnitude shorter than the simulation time needed to study transport physics. For this reason, most Vlasov–Fokker–Planck codes used to study electron transport in laser plasmas rely on simplified models of the laser–plasma coupling. Their underlying assumptions nominally preclude their use for modeling laser light having short-scale structure in space or time, such as broadband lasers. In this work, we derive a more general computational framework suitable for arbitrarily structured laser fields. Furthermore, our approach is based on an extended set of Vlasov–Fokker–Planck equations that separately solve for the low- and high-frequency plasma response. We implement these extended Vlasov–Fokker–Planck equations in the spherical harmonic code K2 and demonstrate the performance of the method on several laser absorption test problems, with particular attention to the judicious selection of time steps, time integrators, and spherical harmonic truncation, according to the intensity and spectrum of the laser light under consideration. Comparison with the widely used Langdon absorption operator shows the Langdon operator performs remarkably well for predicting laser heating in the simple cases considered here, even in situations that would seem to violate its underlying assumptions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Homogeneous superconducting gap in Dy Ba 2 Cu 3 O 7 - δ synthesized by oxide molecular beam epitaxy

Much of what is known about high-temperature cuprate superconductors stems from studies based on two surface analytical tools, angle-resolved photoemission spectroscopy (ARPES) and spectroscopic imaging scanning tunneling microscopy (SI-STM). A question of general interest is whether and when the surface properties probed by ARPES and SI-STM are representative of the intrinsic properties of bulk materials. We find this question is prominent in thin films of a rarely studied cuprate Dy Ba 2 Cu 3 O 7 - δ DBCO). We synthesize DBCO films by oxide molecular beam epitaxy and study them by in situ ARPES and SI-STM. Both ARPES and SI-STM show that the surface DBCO layer is different from the bulk of the film—it is heavily underdoped, while the doping level in the bulk is close to optimal doping evidenced by bulk-sensitive mutual inductance measurements. ARPES shows the typical electronic structure of a heavily underdoped CU O 2 plane and two sets of one-dimensional bands originating from the CuO chains with one of them gapped. SI-STM reveals two different energy scales in the local density of states, with one (at ~ 18 meV ) corresponding to the superconductivity and the other one (at ~ 90 meV ) to the pseudogap. Finally, while the pseudogap shows large variations over the length scale of a few nanometers, the superconducting gap is very homogeneous. This indicates that the pseudogap and superconductivity are of different origins.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Interpreting and Stabilizing Machine-Learning Parametrizations of Convection

Neural networks are a promising technique for parameterizing subgrid-scale physics (e.g., moist atmospheric convection) in coarse-resolution climate models, but their lack of interpretability and reliability prevents widespread adoption. For instance, it is not fully understood why neural network parameterizations often cause dramatic instability when coupled to atmospheric fluid dynamics. This paper introduces tools for interpreting their behavior that are customized to the parameterization task. First, we assess the nonlinear sensitivity of a neural network to lower-tropospheric stability and the midtropospheric moisture, two widely studied controls of moist convection. Second, we couple the linearized response functions of these neural networks to simplified gravity wave dynamics, and analytically diagnose the corresponding phase speeds, growth rates, wavelengths, and spatial structures. To demonstrate their versatility, these techniques are tested on two sets of neural networks, one trained with a super-parameterized version of the Community Atmosphere Model (SPCAM) and the second with a near-global cloud-resolving model (GCRM). Additionally, even though the SPCAM simulation has a warmer climate than the cloud-resolving model, both neural networks predict stronger heating/drying in moist and unstable environments, which is consistent with observations. Moreover, the spectral analysis can predict that instability occurs when GCMs are coupled to networks that support gravity waves that are unstable and have phase speeds larger than 5 m s -1 . In contrast, standing unstable modes do not cause catastrophic instability. Using these tools, differences between the SPCAM-trained versus GCRM-trained neural networks are analyzed, and strategies to incrementally improve both of their coupled online performance unveiled.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

ELECTRON COOLING USING A PULSED AND DITHERING BEAM FROM AN ELECTROSTATIC ELECTRON COOLER

Electron cooling continues to be an invaluable technique to reduce and maintain the emittance in hadron storage rings such as the EIC and EICC where stochastic cooling is inefficient and radiative cooling is negligible. Extending the energy range of electron coolers beyond what is feasible with the conventional, electrostatic approach necessitates the use of RF fields for acceleration and, thus, a bunched electron beam. To experimentally investigate how the relative time structure of the two beams affects the cooling properties, we have set up a pulsed-beam cooling device by adding a synchronized pulsing circuit to the conventional electron source of the CSRm cooler at Institute of Modern Physics. Using both constant and modulated synchronization between electron pulses and ion bunches, we have measured the effects of the electron bunch length and longitudinal ion focusing strength on the temporal evolution of the longitudinal and transverse ion beam profile and demonstrated the detrimental effect of timing jitter as predicted by space-charge theory and simulations. Our experiment suggests a need for further investigations of specific aspects of bunched cooling such as synchro-betatron coupling and phase dithering effects when using short electron bunches to cool longer ion bunches. However, given the comparatively long IBS lifetime of higher-energy proton storage rings like the EIC, slow dithering could potentially provide an option to save cost on the electron cooler linac.

Wang, Haipeng↗
DOE OSTI2021

Demonstration of Electron Cooling using a Pulsed Beam from an Electrostatic Electron Cooler

Electron cooling continues to be an invaluable technique to reduce and maintain the emittance in hadron storage rings in cases where stochastic cooling is inefficient and radiative cooling is negligible. Extending the energy range of electron coolers beyond what is feasible with the conventional, electrostatic approach necessitates the use of RF fields for acceleration and, thus, a bunched electron beam. To experimentally investigate how the relative time structure of the two beams affects the cooling properties, we have set up a pulsed-beam cooling device by adding a synchronized pulsing circuit to the conventional electron source of the CSRm cooler at Institute of Modern Physics *. We show the effect of the electron bunch length and longitudinal ion focusing strength on the temporal evolution of the longitudinal and transverse ion beam profile and demonstrate the detrimental effect of timing jitter as predicted by theory and simulations. Compared to actual RF-based coolers, the simplicity and flexibility of our setup will facilitate further investigations of specific aspects of bunched cooling such as synchro-betatron coupling and phase dithering.

Bruker, M. W.↗
DOE OSTI2021

Demonstration of Electron Cooling using a Pulsed Beam from an Electrostatic Electron Cooler

Electron cooling continues to be an invaluable technique to reduce and maintain the emittance in hadron storage rings in cases where stochastic cooling is inefficient and radiative cooling is negligible. Extending the energy range of electron coolers beyond what is feasible with the conventional, electrostatic approach necessitates the use of RF fields for acceleration and, thus, a bunched electron beam. To experimentally investigate how the relative time structure of the two beams affects the cooling properties, we have set up a pulsed-beam cooling device by adding a synchronized pulsing circuit to the conventional electron source of the CSRm cooler at Institute of Modern Physics *. We show the effect of the electron bunch length and longitudinal ion focusing strength on the temporal evolution of the longitudinal and transverse ion beam profile and demonstrate the detrimental effect of timing jitter as predicted by theory and simulations. Compared to actual RF-based coolers, the simplicity and flexibility of our setup will facilitate further investigations of specific aspects of bunched cooling such as synchro-betatron coupling and phase dithering.

Bruker, M. W.↗
DOE OSTI2021

The TW Hya Rosetta Stone Project. I. Radial and Vertical Distributions of DCN and DCO{sup +}

Molecular D/H ratios are frequently used to probe the chemical past of solar system volatiles. Yet it is unclear which parts of the solar nebula hosted an active deuterium fractionation chemistry. To address this question, we present 0."2–0."4 Atacama Large Millimeter/submillimeter Array (ALMA) observations of DCO{sup +} and DCN 2–1, 3–2, and 4–3 toward the nearby protoplanetary disk around TW Hya, taken as part of the TW Hya Rosetta Stone project, augmented with archival data. DCO{sup +} is characterized by an excitation temperature of ~40 K across the 70 au radius pebble disk, indicative of emission from a warm, elevated molecular layer. Tentatively, DCN is present at even higher temperatures. Both DCO{sup +} and DCN present substantial emission cavities in the inner disk, while in the outer disk the DCO{sup +} and DCN morphologies diverge: most DCN emission originates from a narrow ring peaking around 30 au, with some additional diffuse DCN emission present at larger radii, while DCO{sup +} is present in a broad structured ring that extends past the pebble disk. Based on a set of simple parametric disk abundance models, these emission patterns can be explained by a near-constant DCN abundance exterior to the cavity, and an increasing DCO{sup +} abundance with radius. In conclusion, the ALMA observations reveal an active deuterium fractionation chemistry in multiple disk regions around TW Hya, but not in the cold planetesimal-forming midplane and in the inner disk. More observations are needed to explore whether deuterium fractionation is actually absent in these latter regions, and if its absence is a common feature or something peculiar to the old TW Hya disk.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Semi-Analytical Hierarchical Bayesian Inference of Nonlinear Model Structure in Stochastic Dynamics: Applied to Compartmental Models of Infectious Diseases

A Bayesian computational framework for parsimonious inference in stochastic nonlinear dynamical systems is presented. This framework enables the concurrent estimation of system states, time-varying parameters, time-invariant parameters, and the optimal sparsity structure of the model parameters. Because differential equation-based models are often simplified mechanistic or phenomenological representations, robust inference from noisy measurement data requires explicit treatment of model error and uncertainty. Model error and time-varying parameters can be represented as random processes, enabling inference while making minimal assumptions about the underlying sources of discrepancy and variability. Adopting stochastic differential equation representations affords the model significant flexibility, but can also render it susceptible to overfitting during statistical inversion, where the inferred model may track noise rather than the underlying signal. To alleviate the effects of overfitting and to enable the discovery of the optimal sparse representation of the time-invariant parameters, a Bayesian sparse learning algorithm is embedded within the framework. This sparse learning framework adopts an approximate hierarchical Bayesian setting defined by a series of semi-analytical expressions. The model structure inference framework is validated using a stochastic compartmental model for tracking and forecasting active cases of an infectious disease. Compartmental models describe population-level infectious disease dynamics through interactions among population fractions grouped by disease state. Mathematically, such models consist of a system of coupled ordinary differential equations. This example adopts an expressive compartmental model that includes multiple possible interactions between disease states, motivated by early uncertainty surrounding COVID-19 reinfection dynamics and their implications for long-term epidemic forecasting. The sparse learning exercise permits the inference of a priori unknown epidemiological dynamics from simulated public health data, discovering the nested compartmental model that optimizes the trade-off between average data-fit and model complexity. It is shown that inducing sparsity among the model parameters eliminates redundant interactions between compartments, equivalently revealing the optimal coupling structure between differential equations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Exploring Causal Physical Mechanisms via Non-Gaussian Linear Models and Deep Kernel Learning: Applications for Ferroelectric Domain Structures

Rapid emergence of multimodal imaging in scanning probe, electron, and optical microscopies has brought forth the challenge of understanding the information contained in these complex data sets, targeting the intrinsic correlations between different channels, and further exploring the underpinning causal physical mechanisms. Here, we develop such an analysis framework for Piezoresponse Force Microscopy. We argue that under certain conditions, we can bootstrap experimental observations with the prior knowledge of materials structure to get information on certain nonobserved properties, and demonstrate linear causal analysis for PFM observables. We further demonstrate that the strength of individual causal links between complex descriptors can be ascertained using the deep kernel learning (DKL) model. In this DKL analysis, we use the prior information on domain structure within the image to predict the physical properties. This analysis demonstrates the correlative relationships between morphology, piezoresponse, elastic property, etc., at nanoscale. The prediction of morphology and other physical parameters illustrates a mutual interaction between surface condition and physical properties in ferroelectric materials. Overall, this analysis is universal and can be extended to explore the correlative relationships of other multichannel data sets, and allow for high-fidelity reconstruction of underpinning functionalities and physical mechanisms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Super-resonant dark matter

We introduce Super-Resonant Dark Matter , a model of self-interacting dark matter based on the low energy effective theory of supersymmetric QCD. The structure of the theory ensures a resonant enhancement of the self-interactions of the low energy mesons, since their mass ratio is set by the number of colors and flavors. The velocity dependence of the resonantly enhanced self-interactions allows such theories to accommodate puzzles in small scale structure that arise from dark matter halos of different sizes. The dark matter mass is then predicted to be around 3–4 MeV, with its abundance set by freeze-in via a kinetically mixed dark photon.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Shape-constrained Input Estimation for Efficient Multi-shaker Vibration Testing

Multi-shaker vibration testing is gaining interest from structural dynamics test engineers as it can provide a much more accurate match to complicated field vibration responses than traditional single-axis shaker tests. However, the force capabilities of the small modal shakers typically used in multi-shaker vibration tests has limited the achievable response levels. To date, most multi-shaker vibration tests have been performed using a variety of standard, commercially-available control systems. While these control systems are adequate for a wide range of multiple-input/multiple-output tests, their control algorithms have not been tailored for the specific problem of multi-shaker vibration tests: efficiently coordinating the various shakers to work together to achieve a desired response. Here, a new input estimation algorithm is developed and demonstrated using simulations and actual test data. This algorithm, dubbed shape-constrained input estimation, is shown to effectively coordinate multiple shakers using a set of constraint vectors based on the deflection shapes of the test structure. This is accomplished by using the singular vector shapes of the system frequency response matrix, which allows the constraint vectors to automatically change as a function of frequency. Simulation and test results indicate a significant reduction in the input forces required to achieve a desired response. Finally, the results indicate that shape-constrained input estimation is an effective method to achieve higher response levels from limited shaker forces which will enable higher level multi-shaker vibration tests to be performed.

42 ENGINEERING↗
DOE OSTI2021

Dynamic restructuring of supported metal nanoparticles and its implications for structure insensitive catalysis

Some fundamental concepts of catalysis are not fully explained but are of paramount importance for the development of improved catalysts. An example is the concept of structure insensitive reactions, where surface-normalized activity does not change with catalyst metal particle size. Here we explore this concept and its relation to surface reconstruction on a set of silica-supported Ni metal nanoparticles (mean particle sizes 1–6 nm) by spectroscopically discerning a structure sensitive (CO 2 hydrogenation) from a structure insensitive (ethene hydrogenation) reaction. Using state-of-the-art techniques, inter alia in-situ STEM, and quick-X-ray absorption spectroscopy with sub-second time resolution, we have observed particle-size-dependent effects like restructuring which increases with increasing particle size, and faster restructuring for larger particle sizes during ethene hydrogenation while for CO 2 no such restructuring effects were observed. Furthermore, a degree of restructuring is irreversible, and we also show that the rate of carbon diffusion on, and into nanoparticles increases with particle size. We finally show that these particle size-dependent effects induced by ethene hydrogenation, can make a structure sensitive reaction (CO 2 hydrogenation), structure insensitive. We thus postulate that structure insensitive reactions are actually apparently structure insensitive, which changes our fundamental understanding of the empirical observation of structure insensitivity.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2020

A gravo-aeroelastically scaled wind turbine rotor at field-prototype scale with strict structural requirements

A new sub-scale field-prototype design solution is developed to realize the dynamics, structural response, and distributed loads (gravitational, aerodynamic, centrifugal) that are characteristic of a full-scale large, modern wind turbine rotor. Prior work in sub-scale wind turbine testing has focused on matching aerodynamic/aero-elastic characteristics of full-scale rotors at wind tunnel scale. However, large-scale rotor designs must expand beyond this limited set of scaling parameters for cost-effective prototyping and meet strict requirements for structural safety for field testing. Here, the challenge lies in producing a structural design meeting two competing objectives: novel scaling objectives that prescribe the sub-scale blade to have low mass and stiffness; and traditional structural safety objectives that drive the design to have higher stiffness and mass. A 20% gravo-aeroelastically scaled wind turbine blade is developed successfully that satisfies these competing objectives. First, it achieved close agreement for non-dimensional tip deflection and flap-wise blade frequency (both within 2.1%) with a blade mass distribution constrained to produce target gravitational and centrifugal loads. Second, the entire blade structure was optimized to ensure a safe, manufacturable solution meeting strict strength requirements for a testing site that can experience up to 45 m/s wind gusts. The prototype-scale blade was fabricated and successfully proof-load tested.

17 WIND ENERGY↗
DOE OSTI2020

Combinatorial Exploration and Mapping of Phase Transformation in a Ni–Ti–Co Thin Film Library

Combinatorial synthesis and high-throughput characterization of a Ni–Ti–Co thin film materials library are reported for exploration of reversible martensitic transformation. The library was prepared by magnetron co-sputtering, annealed in vacuum at 500 °C without atmospheric exposure, and evaluated for shape memory behavior as an indicator of transformation. Composition, structure, and transformation behavior of the 177 pads in the library were characterized using high-throughput wavelength dispersive spectroscopy (WDS), X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD), and four-point probe temperature-dependent resistance (R(T)) measurements. A new, expanded composition space having phase transformation with low thermal hysteresis and Co > 10 at. % is found. Unsupervised machine learning methods of hierarchical clustering were employed to streamline data processing of the large XRD and XPS data sets. Through cluster analysis of XRD data, we identified and mapped the constituent structural phases. Finally, composition–structure–property maps for the ternary system are made to correlate the functional properties to the local microstructure and composition of the Ni–Ti–Co thin film library.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Experimental studies at low $Q^2$ of the spin structure of the nucleon at Jefferson Lab

We summarize the experimental program of Jefferson Lab that studies the nucleon spin structure at low $Q^2$. This program completes the precise experimental mapping of the nucleon spin structure functions $g_1(\nu, Q^2)$ and $g_2(\nu, Q^2)$ and their moments started at SLAC, CERN and DESY at high $Q^2$, and continued at Jefferson Lab at intermediate $Q^2$. The results presented cover the domain where Chiral Effective Field Theory ($\chi$EFT) should describe the strong interaction. They provide a comprehensive set of benchmark measurements for $\chi$EFT. The preliminary conclusion is that nucleon spin structure data are still challenging for $\chi$EFT in spite of the notable improvements in these calculations.

Deur, Alexandre↗