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At least 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Improving E3SM Land Model Photosynthesis Parameterization via Satellite SIF, Machine Learning, and Surrogate Modeling

The parameterization of key photosynthesis parameters is one of the key uncertain sources in modeling ecosystem gross primary productivity (GPP). Solar-induced chlorophyll fluorescence (SIF) offers a good proxy for GPP since it marks the actual process of photosynthesis; while machine learning (ML) provides a robust approach to model the GPP-SIF relationship. Here, we trained the boosted regressing tree (BRT) and the Random Forest ML models with Greenhouse Gases Observing Satellite SIF data and in situ GPP observations from 49 eddy covariance towers. These trained ML GPP-SIF models were fed into the Energy Exascale Earth System Model (E3SM) Land Model (ELM) to generate ELM-simulated global SIF estimates, which were then benchmarked against satellite SIF observations with a surrogate modeling approach. Our results indicated good modeling performance of the ML-based GPP-SIF relationship. The ELM model when fed with the ML GPP-SIF models also can well predict the spatial-temporal variations in SIF. We also found high model accuracy for the surrogate modeling. Model parameter sensitivity analysis suggested that the fraction of leaf nitrogen in RuBisCO (flnr) is the most sensitive parameter to the SIF; other sensitive parameters include the Ball-Berry stomatal conductance slope (mbbopt) and the vcmax entropy (vcmaxse). The posterior uncertainty in simulated GPP was greatly reduced after benchmarking, and the model produced improved spatial patterns of mean GPP relative to FLUXCOM GPP. Our integrated approach provides a new avenue for improving land models and using remote-sensing SIF, which can be further improved in the future with more ground- and satellite-based observations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Assessing Clouds in GFDL's AM4.0 With Different Microphysical Parameterizations Using the Satellite Simulator Package COSP

We evaluate cloud simulations using satellite simulators against multiple observational data sets. These simulators have been run within the Geophysical Fluid Dynamics Laboratory's Atmosphere Model version 4.0 (AM4.0), as well as an alternative configuration where a fully two‐moment Morrison‐Gettelman cloud microphysical parameterization with prognostic precipitation (MG2) is applied, denoted as AM4‐MG2. The modeled cloud spatial distributions, vertical profiles, phase partitioning, cloud‐to‐precipitation transitions, and radiative effects compare reasonably well with satellite observations. Model biases include the under‐prediction of total and low‐level clouds, especially optically thin/intermediate clouds with cloud optical depth of less than 23, but the over‐prediction of thick clouds, indicating “too few, too bright” biases. These biases counteract each other, and give rise to reasonable estimates of cloud radiative effects. The underestimate of low‐level clouds is associated with too early and too frequent drizzle/precipitation formation. The precipitation bias is improved in AM4‐MG2, where the autoconversion scheme initiates the precipitation more realistically. There also exist discrepancies between models and observations for midlevel and high‐level clouds. Additional biases include the underestimate of liquid cloud fraction and the overestimate of ice cloud fraction.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Stable Machine‐Learning Parameterization of Subgrid Processes in a Comprehensive Atmospheric Model Learned From Embedded Convection‐Permitting Simulations

Modern climate projections often suffer from inadequate spatial and temporal resolution due to computational limitations, resulting in inaccurate representations of sub-grid processes. A promising technique to address this is the multiscale modeling framework (MMF), which embeds a kilometer-resolution cloud-resolving model (CRM) within each atmospheric column of a host climate model to replace traditional convection and cloud parameterizations. Machine learning offers a unique opportunity to make MMF more accessible by emulating the embedded CRM and reducing its substantial computational cost. Although many studies have demonstrated proof-of-concept success of achieving stable hybrid simulations, it remains a challenge to achieve near operational-level success with real geography and comprehensive variable emulation that includes, for example, explicit cloud condensate coupling. In this study, we present a stable hybrid model capable of integrating for at least 5 years with near operational-level complexity, including coarse-grid geography, seasonality, explicit cloud condensate and wind predictions, and land coupling. Our model demonstrates skillful online performance, achieving a 5-year zonal mean tropospheric temperature bias within 2 K, water vapor bias within 1 g/kg, and a precipitation root mean square error of 0.96 mm/day. Key factors contributing to our online performance include an expressive U-Net architecture and physical thermodynamic constraints for microphysics. With microphysical constraints mitigating unrealistic cloud formation, our work is the first to demonstrate realistic multi-year cloud condensate climatology under the MMF framework. Despite these advances, online diagnostics reveal persistent biases in certain regions, highlighting the need for innovative strategies to further optimize online performance.

Hu, Zeyuan [NVIDIA Corporation, Santa Clara, CA (U↗
DOE OSTI2024

Revealing the hidden structure of disordered materials by parameterizing their local structural manifold

Abstract Durable interest in developing a framework for the detailed structure of glassy materials has produced numerous structural descriptors that trade off between general applicability and interpretability. However, none approach the combination of simplicity and wide-ranging predictive power of the lattice-grain-defect framework for crystalline materials. Working from the hypothesis that the local atomic environments of a glassy material are constrained by enthalpy minimization to a low-dimensional manifold in atomic coordinate space, we develop a generalized distance function, the Gaussian Integral Inner Product (GIIP) distance, in connection with agglomerative clustering and diffusion maps, to parameterize that manifold. Applying this approach to a two-dimensional model crystal and a three-dimensional binary model metallic glass results in parameters interpretable as coordination number, composition, volumetric strain, and local symmetry. In particular, we show that a more slowly quenched glass has a higher degree of local tetrahedral symmetry at the expense of cyclic symmetry. While these descriptors require post-hoc interpretation, they minimize bias rooted in crystalline materials science and illuminate a range of structural trends that might otherwise be missed.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Parameterization of interatomic potential by genetic algorithms: A case study

A framework for Genetic Algorithm based methodology is developed to systematically obtain and optimize parameters for interatomic force field functions for MD simulations by fitting to a reference data base. This methodology is applied to the fitting of ThO{sub 2} (CaF{sub 2} prototype) – a representative of ceramic based potential fuel for nuclear applications. The resulting GA optimized parameterization of ThO{sub 2} is able to capture basic structural, mechanical, thermo-physical properties and also describes defect structures within the permissible range.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2015

A review on the relativistic effective field theory with parameterized couplings for nuclear matter and neutron stars

Nuclear science has developed many excellent theoretical models for many-body systems in the domain of the baryon-meson strong interaction for the nucleus and nuclear matter at low, medium and high densities. However, a full microscopic understanding of nuclear systems in the extreme density domain of compact stars is still lacking. The aim of this contribution is to shed some light on open questions facing the nuclear many-body problem at the very high density domain. Here we focus our attention on the conceptual issue of naturalness and its role in shaping the baryon-meson phase space dynamics in the description of the equation of state (EoS) of nuclear matter and neutrons stars. In particular, in order to stimulate possible new directions of research, we discuss relevant aspects of a recently developed relativistic effective theory for nuclear matter within Quantum Hadrodynamics (QHD) with genuine many-body forces and derivative natural parametric couplings. Among other topics we discuss in this work the connection of this theory with other known effective QHD models of the literature and its potentiality in describing a new physics for dense matter. The model with parameterized couplings exhausts the whole fundamental baryon octet (n, p, Σ{sup −}, Σ{sup 0}, Σ{sup +}, Λ, Ξ{sup −}, Ξ{sup 0}) and simulates n-order corrections to the minimal Yukawa baryon couplings by considering nonlinear self-couplings of meson fields and meson-meson interaction terms coupled to the baryon fields involving scalar-isoscalar (σ, σ∗), vector-isoscalar (ω, Φ), vector-isovector (ϱ) and scalar-isovector (δ) virtual sectors. Following recent experimental results, we consider in our calculations the extreme case where the Σ{sup −} experiences such a strong repulsion that its influence in the nuclear structure of a neutron star is excluded at all. A few examples of calculations of properties of neutron stars are shown and prospects for the future are discussed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2016

Precise parameterization of the recombination velocity at passivated phosphorus doped surfaces

We investigate the surface recombination velocity S{sub p} at the silicon-dielectric interface of phosphorus-doped surfaces for two industrially relevant passivation schemes for crystalline silicon solar cells. A broad range of surface dopant concentrations together with a high accuracy of evaluating the latter is achieved by incremental back-etching of the surface. The analysis of lifetime measurements and the simulation of the surface recombination consistently apply a set of well accepted models, namely, the Auger recombination by Richter et al. [Phys. Rev. B 86, 1–14 (2012)], the carrier mobility by Klaassen [Solid-State Electron. 35, 953–959 (1992); 35, 961–967 (1992)], the intrinsic carrier concentration for undoped silicon by Altermatt et al. [J. Appl. Phys. 93, 1598–1604 (2003)], and the band-gap narrowing by Schenk [J. Appl. Phys. 84, 3684–3695 (1998)]. The results show an increased S{sub p} at textured in respect to planar surfaces. The obtained parameterizations are applicable in modern simulation tools such as EDNA [K. R. McIntosh and P. P. Altermatt, in Proceedings of the 35th IEEE Photovoltaic Specialists Conference, Honolulu, Hawaii, USA (2010), pp. 1–6], PC1Dmod [Haug et al., Sol. Energy Mater. Sol. Cells 131, 30–36 (2014)], and Sentaurus Device [Synopsys, Sentaurus TCAD, Zürich, Switzerland] as well as in the analytical solution under the assumption of local charge neutrality by Cuevas et al. [IEEE Trans. Electron Devices 40, 1181–1183 (1993)].

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

Relativistic quasiparticle band structures of Mg 2 Si, Mg 2 Ge, and Mg 2 Sn: Consistent parameterization and prediction of Seebeck coefficients

We apply density functional and many-body perturbation theory calculations to consistently determine and parameterize the relativistic quasiparticle band structures of Mg 2 Si, Mg 2 Ge, and Mg 2 Sn, and predict the Seebeck coefficient as a function of doping and temperature. The quasiparticle band gaps, including spin-orbit coupling effects, are determined to be 0.728 eV, 0.555 eV, and 0.142 eV for Mg 2 Si, Mg 2 Ge, and Mg 2 Sn, respectively. The inclusion of the semicore electrons of Mg, Ge, and Sn in the valence is found to be crucial for the accurate determination of the band gaps of Mg 2 Ge and Mg 2 Sn. We also developed a Luttinger-Kohn Hamiltonian and determined a set of band parameters to model the near-edge relativistic quasiparticle band structure consistently for all three compounds that can be applied for thermoelectric device simulations. Our calculated values for the Seebeck coefficient of all three compounds are in good agreement with the available experimental data for a broad range of temperatures and carrier concentrations. Our findings suggest that quasiparticle corrections are necessary for the accurate determination of Seebeck coefficients at high temperatures at which bipolar transport becomes important.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Accurate and efficient parameterization of an atomic cluster expansion (ACE) potential for ammonia under extreme conditions

We present a machine learning interatomic potential for ammonia designed to capture its complex multiphase behavior, including both molecular and superionic phases. The potential is based on the atomic cluster expansion (ACE) formulation and has been parameterized to facilitate high-fidelity molecular dynamics simulations of ammonia under extreme conditions, for pressures up to 100 GPa and for temperatures above 500 K and up to 6000 K. A diverse range of configurations was generated through high-quality ab initio molecular dynamics simulations, covering insulating and superionic ice phases, liquid ammonia, molecular nitrogen (N 2 ) and hydrogen (H 2 ), and metastable compounds that form upon dissociation, including $NH^{+}_{4}$, $H^{+}_{3}$, N 2 H 4 , and N 3 H. We demonstrate that the ammonia ACE potential accurately reproduces experimental and density functional theory predicted isotherms and Hugoniots. Crucially, the potential is able to capture the intricate phase behavior of ammonia, including the transition from insulating molecular fluid to the superionic phase. This work provides a robust interatomic potential that can be used for large-scale, accurate simulations of ammonia under extreme thermodynamic conditions, offering a powerful tool for investigating its behavior in various phases and applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Parameterized anomalous transport model for current-carrying collisionless plasmas in pulsed power inertial confinement fusion

Current delivery in pulsed power inertial confinement fusion is influenced by collisionless current-carrying microturbulent plasmas, which are sourced from electrode surfaces. In this setting, the lower hybrid drift instability—triggered by plasma acceleration—is a leading candidate driver of difficult-to-predict momentum and energy transport. To characterize the nonlinear state of the microturbulent plasma, a parameterized anomalous transport model is developed for the instability, with analytic formulas for anomalous collision frequency, resistivity, and species heating rates. The formulas are expressed in terms of linear-theory variables and four dimensionless parameters that characterize the macroscopic plasma state. The model is built on linear theory analysis, power law analysis, and quasilinear theory analysis, and is validated using a series of nonlinear continuum kinetic Vlasov–Poisson simulations. The theoretical and computational investigation demonstrates that the anomalous collision frequency associated with the instability can be reliably approximated, within about a factor of five or better, by the unscaled linear theory growth rate of the fastest-growing wavenumber mode. This finding enables efficient calculation of anomalous resistivity and species heating rates over a wide range of plasma conditions, resulting in improved predictive capabilities.

Complex functions↗
DOE OSTI2018

Peptide design by optimization on a data-parameterized protein interaction landscape

Many applications in protein engineering require optimizing multiple protein properties simultaneously, such as binding one target but not others or binding a target while maintaining stability. Such multistate design problems require navigating a high-dimensional space to find proteins with desired characteristics. A model that relates protein sequence to functional attributes can guide design to solutions that would be hard to discover via screening. In this work, we measured thousands of protein–peptide binding affinities with the high-throughput interaction assay amped SORTCERY and used the data to parameterize a model of the alpha-helical peptide-binding landscape for three members of the Bcl-2 family of proteins: Bcl-x L , Mcl-1, and Bfl-1. We applied optimization protocols to explore extremes in this landscape to discover peptides with desired interaction profiles. Computational design generated 36 peptides, all of which bound with high affinity and specificity to just one of Bcl-x L , Mcl-1, or Bfl-1, as intended. We designed additional peptides that bound selectively to two out of three of these proteins. The designed peptides were dissimilar to known Bcl-2–binding peptides, and high-resolution crystal structures confirmed that they engaged their targets as expected. Excellent results on this challenging problem demonstrate the power of a landscape modeling approach, and the designed peptides have potential uses as diagnostic tools or cancer therapeutics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Weighing neutrinos in the scenario of vacuum energy interacting with cold dark matter: application of the parameterized post-Friedmann approach

We constrain the neutrino mass in the scenario of vacuum energy interacting with cold dark matter by using current cosmological observations. To avoid the large-scale instability problem in interacting dark energy models, we employ the parameterized post-Friedmann (PPF) approach to do the calculation of perturbation evolution, for the Q = β H ρ{sub c} and Q = β H ρ{sub Λ} models. The current observational data sets used in this work include Planck (cosmic microwave background), BSH (baryon acoustic oscillations, type Ia supernovae, and Hubble constant), and LSS (redshift space distortions and weak lensing). According to the constraint results, we find that β > 0 at more than 1σ level for the Q = β H ρ{sub c} model, which indicates that cold dark matter decays into vacuum energy; while β = 0 is consistent with the current data at 1σ level for the Q = β H ρ{sub Λ} model. Taking the ΛCDM model as a baseline model, we find that a smaller upper limit, ∑ m {sub ν} < 0.11 eV (2σ), is induced by the latest BAO BOSS DR12 data and the Hubble constant measurement H {sub 0} = 73.00 ± 1.75 km s{sup −1} Mpc{sup −1}. For the Q = β H ρ{sub c} model, we obtain ∑ m {sub ν}<0.20 eV (2σ) from Planck+BSH. For the Q = β H ρ{sub Λ} model, ∑ m {sub ν}<0.10 eV (2σ) and ∑ m {sub ν}<0.14 eV (2σ) are derived from Planck+BSH and Planck+BSH+LSS, respectively. We show that these smaller upper limits on ∑ m {sub ν} are affected more or less by the tension between H {sub 0} and other observational data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Sweeping Horndeski canvas: new growth-rate parameterization for modified-gravity theories

We propose and numerically validate a new fitting formula that is sufficiently accurate to model the growth of structure in Horndeski theories of modified gravity for upcoming Stage IV and V large-scale structure surveys. Based on an analysis of more than 18,000 Horndeski models and adopting the popular parameterization of the growth rate f(z) = Ω M (z) γ , we generalize the constant growth index γ to a two-parameter redshift-dependent quantity, γ(z), that more accurately fits these models. We demonstrate that the functional form γ(z) = γ 0 + γ 0 z 2 /(1 + z) improves the median χ2 of the fit to viable Horndeski models by a factor of ~40 relative to that of a constant γ, and is sufficient to obtain unbiased results even for precise measurements expected in Stage IV and V surveys. Finally, we constrain the parameters of the new fitting formula using current cosmological data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Divertor-safe nonlinear burn control based on a SOLPS parameterized core-edge model for ITER

Abstract For ITER operations, the range of desirable burning-plasma regimes with high fusion power output will be restricted by various operational constraints. These constraints include the saturation of ITER’s various heating and fueling actuators such as the neutral beam injectors, the ion and electron cyclotron heating systems, the gas puffing system, and the deuterium–tritium pellet injectors. In addition to these actuator constraints, the H-mode power threshold, divertor detachment, and the heat load on the divertor targets may apply limitations to ITER’s operational space. In this work, Plasma Operation Contour (POPCON) plots that map the aforementioned constraints to the temperature-density space are used to investigate which constraints are most limiting towards accessing regimes with high fusion power output. The presented POPCON plots are based on a control-oriented core-edge model that couples the nonlinear density and energy response models for the core-plasma region with SOLPS4.3 parameterizations for conditions in the edge-plasma regions (scrape-off-layer and divertor). Using this control-oriented core-edge model, a nonlinear burn controller, which aims to regulate the plasma temperature and density in the core-plasma region, is constructed in this work. This controller is augmented with an online optimization scheme that governs the control references such that the plasma can be guided towards regimes with high fusion powers while protecting the divertor targets from dangerously high heat loads. A closed-loop simulation study illustrates the capability of this burn control scheme.

Physics↗
DOE OSTI2022

Parameterization of electron attachment rate constants for impurities in LArTPC detectors

The ability of free electrons to drift long distances at high velocities in pure liquid argon under an applied electric field has been exploited for the past forty years to implement detectors with increasingly larger volumes for high energy physics research. The attachment of free electrons to impurities in the LAr is an important limit on the free instrumented volume of these extremely large detectors, and impurity concentrations as small as 100 ppt can reduce their resolution and efficiency. In this paper, we summarize the electron attachment rate constants as a function of the applied electric field, for common impurities in LArTPCs, obtained from data in the literature. Here, we further provide analytical functions to parameterize the data, which are useful to compare with new measurements, to model and analyze the performance of existing detectors, and to predict the performance of new detectors.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Large gradients via correlation in random parameterized quantum circuits

Scaling of variational quantum algorithms to large problem sizes requires efficient optimization of random parameterized quantum circuits. For such circuits with uncorrelated parameters, the presence of exponentially vanishing gradients in cost function landscapes is an obstacle to optimization by gradient descent methods. In this work, we prove that reducing the dimensionality of the parameter space by utilizing circuit modules containing spatially or temporally correlated gate layers can allow one to circumvent the vanishing gradient phenomenon. Here, examples are drawn from random separable circuits and asymptotically optimal variational versions of Grover's algorithm based on the quantum alternating operator ansatz. In the latter scenario, our bounds on cost function variation imply a transition between vanishing gradients and efficient trainability as the number of layers is increased toward $\mathcal{O}\left({2}^{n/2}\right)$, the optimal oracle complexity of quantum unstructured search.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Building spatial symmetries into parameterized quantum circuits for faster training

Abstract Practical success of quantum learning models hinges on having a suitable structure for the parameterized quantum circuit. Such structure is defined both by the types of gates employed and by the correlations of their parameters. While much research has been devoted to devising adequate gate-sets, typically respecting some symmetries of the problem, very little is known about how their parameters should be structured. In this work, we show that an ideal parameter structure naturally emerges when carefully considering spatial symmetries (i.e. the symmetries that are permutations of parts of the system under study). Namely, we consider the automorphism group of the problem Hamiltonian, leading us to develop a circuit construction that is equivariant under this symmetry group. The benefits of our novel circuitstructure, called ORB, are numerically probed in several ground-state problems. We find a consistent improvement (in terms of circuit depth, number of parameters required, and gradient magnitudes) compared to literature circuit constructions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Machine learning visualization tool for exploring parameterized hydrodynamics

We are interested in the computational study of shock hydrodynamics, i.e. problems involving compressible solids, liquids, and gases that undergo large deformation. These problems are dynamic and nonlinear and can exhibit complex instabilities. Due to advances in high performance computing it is possible to parameterize a hydrodynamic problem and perform a computational study yielding $\mathscr{O} (TB)$ of simulation state data. We present an interactive machine learning tool that can be used to compress, browse, and interpolate these large simulation datasets. This tool allows computational scientists and researchers to quickly visualize 'what-if' situations, perform sensitivity analyses, and optimize complex hydrodynamic experiments.

97 MATHEMATICS AND COMPUTING↗