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DOE OSTI2020

Multi-fidelity machine-learning with uncertainty quantification and Bayesian optimization for materials design: Application to ternary random alloys

We present a scale-bridging approach based on a multi-fidelity (MF) machine-learning (ML) framework leveraging Gaussian processes (GP) to fuse atomistic computational model predictions across multiple levels of fidelity. Through the posterior variance of the MFGP, our framework naturally enables uncertainty quantification, providing estimates of confidence in the predictions. We used density functional theory as high-fidelity prediction, while a ML interatomic potential is used as low-fidelity prediction. Practical materials’ design efficiency is demonstrated by reproducing the ternary composition dependence of a quantity of interest (bulk modulus) across the full aluminum–niobium–titanium ternary random alloy composition space. The MFGP is then coupled to a Bayesian optimization procedure, and the computational efficiency of this approach is demonstrated by performing an on-the-fly search for the global optimum of bulk modulus in the ternary composition space. The framework presented in this manuscript is the first application of MFGP to atomistic materials simulations fusing predictions between density functional theory and classical interatomic potential calculations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Reconstruction and uncertainty quantification of lattice Hamiltonian model parameters from observations of microscopic degrees of freedom

The emergence of scanning probe and electron beam imaging techniques has allowed quantitative studies of atomic structure and minute details of electronic and vibrational structure on the level of individual atomic units. These microscopic descriptors, in turn, can be associated with local symmetry breaking phenomena, representing the stochastic manifestation of the underpinning generative physical model. In this work, we explore the reconstruction of exchange integrals in the Hamiltonian for a lattice model with two competing interactions from observations of microscopic degrees of freedom and establish the uncertainties and reliability of such analysis in a broad parameter-temperature space. In contrast to other approaches, we specifically specify a loss function inherent to thermodynamic systems and utilize it to estimate uncertainty in simulated realizations of different models. As an ancillary task, we develop a machine learning approach based on histogram clustering to predict phase diagrams efficiently using a reduced descriptor space. We further demonstrate that reconstruction is possible well above the phase transition and in the regions of parameter space when the macroscopic ground state of the system is poorly defined due to frustrated interactions. This suggests that this approach can be applied to the traditionally complex problems of condensed matter physics such as ferroelectric relaxors and morphotropic phase boundary systems, spin and cluster glasses, and quantum systems once the local descriptors linked to the relevant physical behaviors are known.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2022

Kinetic theory of particle-in-cell simulation plasma and the ensemble averaging technique

Abstract We derive the kinetic theory of fluctuations in physically and numerically stable particle-in-cell (PIC) simulations of electrostatic plasmas. The starting point is the single-time correlation at the start of the simulation between the statistical fluctuations of the weighted densities of macroparticle centers in the plasma particle phase-space. The fluctuations are associated with different initial conditions, typically due to the random initial conditions (in velocity space) of the macroparticles/simulation plasma, assigned according to their initial distribution of probability. The single-time correlations at all time steps and in each spatial grid cell are then determined from the Laplace–Fourier transforms of the discretized Klimontovich-like equation for the macroparticles and Maxwell’s equations for the fields, as computed by modern PIC codes. We recover the expressions for the electrostatic field and the plasma particle density fluctuation autocorrelation spectra as well as the kinetic equations describing the average evolution of PIC-simulated plasma particles, first derived by Langdon (1970b Proc. 4th Conf. Numerical Simulation of Plasmas ) using a test macroparticle approach perturbing a discretized Vlasovian plasma and then averaging the obtained physical quantity over the initial macroparticle velocity distribution. We generalize and extend these results to the modern algorithms in PIC codes using arbitrary macroparticle weights. Analytical estimates of statistical fluctuation amplitudes are derived as a function of the plasma simulation parameters, using the central limit theorem in the limit of a large number of macroparticles per cell. The theory is then used to analyze the ensemble averaging technique of PIC simulations where statistical averages are performed over ensembles of PIC simulations, modeling the same plasma physics problem but using different statistical realizations of the initial distribution functions of the macroparticles. This method is illustrated by linear Landau damping uncovering (from noise, which is usually considered numerical) the physical fluctuations driven by a single small amplitude electrostatic wave perturbing a PIC simulation plasma in equilibrium.

fluctuations correlations↗

DOE OSTI2024

Eliater: a Python package for estimating outcomes of perturbations in biomolecular networks

We introduce Eliater, a Python package for estimating the effect of perturbation of an upstream molecule on a downstream molecule in a biomolecular network. The estimation takes as input a biomolecular network, observational biomolecular data, and a perturbation of interest, and outputs an estimated quantitative effect of the perturbation. We showcase the functionalities of Eliater in a case study of Escherichia coli transcriptional regulatory network.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2019

rascalc : a jackknife approach to estimating single- and multitracer galaxy covariance matrices

ABSTRACT To make use of clustering statistics from large cosmological surveys, accurate and precise covariance matrices are needed. We present a new code to estimate large-scale galaxy two-point correlation function (2PCF) covariances in arbitrary survey geometries that, due to new sampling techniques, runs ∼104 times faster than previous codes, computing finely binned covariance matrices with negligible noise in less than 100 CPU-hours. As in previous works, non-Gaussianity is approximated via a small rescaling of shot noise in the theoretical model, calibrated by comparing jackknife survey covariances to an associated jackknife model. The flexible code, rascalc, has been publicly released, and automatically takes care of all necessary pre- and post-processing, requiring only a single input data set (without a prior 2PCF model). Deviations between large-scale model covariances from a mock survey and those from a large suite of mocks are found to be indistinguishable from noise. In addition, the choice of input mock is shown to be irrelevant for desired noise levels below ∼105 mocks. Coupled with its generalization to multitracer data sets, this shows the algorithm to be an excellent tool for analysis, reducing the need for large numbers of mock simulations to be computed.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Major impacts of widespread structural variation on sorghum

Genetic diversity is critical to crop breeding and improvement, and dissection of the genomic variation underlying agronomic traits can both assist breeding and give insight into basic biological mechanisms. Although recent genome analyses in plants reveal many structural variants (SVs), most current studies of crop genetic variation are dominated by single-nucleotide polymorphisms (SNPs). The extent of the impact of SVs on global trait variation, as well as their utility in genome-wide selection, is not yet understood. In this study, we built an SV data set based on whole-genome resequencing of diverse sorghum lines (n = 363), validated the correlation of photoperiod sensitivity and variety type, and identified SV hotspots underlying the divergent evolution of cellulosic and sweet sorghum. In addition, we showed the complementary contribution of SVs for heritability of traits related to sorghum adaptation. Importantly, inclusion of SV polymorphisms in association studies revealed genotype–phenotype associations not observed with SNPs alone. Three-way genome-wide association studies (GWAS) based on whole-genome SNP, SV, and integrated SNP + SV data sets showed substantial associations between SVs and sorghum traits. The addition of SVs to GWAS substantially increased heritability estimates for some traits, indicating their important contribution to functional allelic variation at the genome level. Our discovery of the widespread impacts of SVs on heritable gene expression variation could render a plausible mechanism for their disproportionate impact on phenotypic variation. This study expands our knowledge of SVs and emphasizes the extensive impacts of SVs on sorghum.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2018

Mixing A -type and G -type B -site antiferromagnetism in A M n 1 - x F e x O 3 ( A = La , Nd )

Using a classical Heisenberg model with anisotropic nearest-neighbor superexchange and uniaxial anisotropy, I investigate the magnetic order of the transition metals on the B sites in AMn 1-x Fe x O 3 (A=La, Nd; x=[0,1]) with Monte Carlo methods. Magnetic parameters are extracted from preexisting experimental data when available and otherwise estimated with calculations. Superexchange energies are compared to density functional theory and anisotropy energies to ligand field theory, and the potential for doping-induced strain affecting these parameters is considered. I find coexistence regions of different types of magnetic order (A-type and G-type antiferromagnetism), regions with distinct magnetic transitions for the two order parameters, noncollinear ground states for intermediate values of x that decrease the amplitude of the ordered moment in a way consistent with experiment for low dopings, and a reproduction of the experimentally determined, nonmonotonic interpolation of the ordering temperatures. The detailed shape of the phase boundaries in (x,T) space is found to be diagnostic of the magnetic interactions. Finally, these results also suggest a ferrimagnetic ground state for the A 2 MnFeO 6 double perovskite with T N ≈270K for A=La and Nd.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2017

Plasma dynamics near critical density inferred from direct measurements of laser hole boring

Here, we use multiframe picosecond optical interferometry to make direct measurements of the hole boring velocity, vHB, of the density cavity pushed forward by a train of CO 2 laser pulses in a near critical density helium plasma. As the pulse train intensity rises, the increasing radiation pressure of each pulse pushes the density cavity forward and the plasma electrons are strongly heated. After the peak laser intensity, the plasma pressure exerted by the heated electrons strongly impedes the hole boring process and the vHB falls rapidly as the laser pulse intensity falls at the back of the laser pulse train. We present a heuristic theory that allows the estimation of the plasma electron temperature from the measurements of the hole boring velocity. Furthermore, the measured values of v HB , and the estimated values of the heated electron temperature as a function of laser intensity are in reasonable agreement with those obtained from two-dimensional numerical simulations.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Source:sink imbalance detected with leaf and canopy-level spectroscopy in a field-grown crop

The finely-tuned balance between sources and sinks determines plant resource partitioning and regulates growth and development. Understanding and measuring metabolic indicators of source or sink limitation forms a vital part of global efforts to increase crop yield for future food security. Here, we measured metabolic profiles of Cucurbita pepo (zucchini) grown in the field under carbon sink limitation and control conditions. We demonstrate that these profiles can be measured non-destructively using hyperspectral reflectance at both leaf and canopy scales. Total non-structural carbohydrates (TNC) increased 82% in sink-limited plants; leaf mass per unit area (LMA) increased 38%; free amino acids increased 22%. Partial least-squares regression models link these measured functional traits with reflectance data, enabling high-throughput estimation of traits comprising the sink limitation response. Leaf- and canopy-scale models for TNC had R 2 values of 0.93 and 0.64 and %RMSE of 13% and 38% respectively. For LMA, R 2 values were 0.91 and 0.60 and %RMSE 7% and 14%; for free amino acids, R 2 was 0.53 and 0.21 with %RMSE 20% and 26%. Remote sensing can enable accurate, rapid detection of sink limitation in the field at the leaf and canopy scale, greatly expanding our ability to understand and measure metabolic responses to stress.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

First-principles study of magnetism and electric field effects in 2D systems

This review article provides a bird's-eye view of what first-principles based methods can contribute to next-generation device design and simulation. After a brief overview of methods and capabilities in the area, the authors focus on published work by their group since 2015 and current work on CrI3. The authors introduce both single- and dual-gate models in the framework of density functional theory and the constrained random phase approximation in estimating the Hubbard U for 2D systems vs their 3D counterparts. A wide range of systems, including graphene-based heterogeneous systems, transition metal dichalcogenides, and topological insulators, and a rich array of physical phenomena, including the macroscopic origin of polarization, field effects on magnetic order, interface state resonance induced peak in transmission coefficients, spin filtration, etc., are covered. For CrI3, the authors present their new results on bilayer systems such as the interplay between stacking and magnetic order, pressure dependence, and electric field induced magnetic phase transitions. The authors find that a bare bilayer CrI3, graphene|bilayer CrI3|graphene, hexagonal boron nitride (h-BN)|bilayer CrI3|h-BN, and h-BN|bilayer CrI3|graphene all have a different response at high field, while at small field, the difference is small except for graphene|bilayer CrI3|graphene. The authors conclude with discussion of some ongoing work and work planned in the near future, with the inclusion of further method development and applications.

Cheng, Hai-Ping (ORCID:0000000159901725)↗

DOE OSTI2022

Optimal band selection for target detection with a LWIR multispectral imager

Multispectral imaging can offer many benefits in cost, complexity, resolution, size, weight, and power, relative to hyperspectral imaging. When designing a multispectral system, spectral bandpasses can be selected using optimization algorithms configured to maximally separate target detection scores between target and background regions. A hyperspectral image (HSI) can serve as the source of data from which band groupings can be tested for optimality. The output of an adaptive cosine estimator target detection algorithm is used in an objective function. Three optimization algorithms are compared: particle swarm, dual annealing, and differential evolution. A global optimum is also found using a brute force approach on the Livermore Computing Syrah supercomputer. Three materials are investigated: calcite, gypsum, and limestone. This is done for 3-, 4-, and 5-band systems. The data originate from a longwave infrared HSI of a material display board. The optimization algorithms were run 30 times for every scenario. Performance statistics (maximum, minimum, mean, standard deviation, and median) based on the separation values are given. Additional characterization was performed using receiver operator characteristic (ROC) curves and the area under the ROC curve. While good performance was obtained for the three optimization algorithms, the dual annealing algorithm produced the highest and most consistent detection separation scores on average.

47 OTHER INSTRUMENTATION↗

DOE OSTI2015

A revised dosimetric characterization of the model S700 electronic brachytherapy source containing an anode-centering plastic insert and other components not included in the 2006 model

Purpose: The model S700 Axxent electronic brachytherapy source by Xoft, Inc., was characterized by Rivard et al. in 2006. Since then, the source design was modified to include a new insert at the source tip. Current study objectives were to establish an accurate source model for simulation purposes, dosimetrically characterize the new source and obtain its TG-43 brachytherapy dosimetry parameters, and determine dose differences between the original simulation model and the current model S700 source design. Methods: Design information from measurements of dissected model S700 sources and from vendor-supplied CAD drawings was used to aid establishment of an updated Monte Carlo source model, which included the complex-shaped plastic source-centering insert intended to promote water flow for cooling the source anode. These data were used to create a model for subsequent radiation transport simulations in a water phantom. Compared to the 2006 simulation geometry, the influence of volume averaging close to the source was substantially reduced. A track-length estimator was used to evaluate collision kerma as a function of radial distance and polar angle for determination of TG-43 dosimetry parameters. Results for the 50 kV source were determined every 0.1 cm from 0.3 to 15 cm and every 1° from 0° to 180°. Photon spectra in water with 0.1 keV resolution were also obtained from 0.5 to 15 cm and polar angles from 0° to 165°. Simulations were run for 10{sup 10} histories, resulting in statistical uncertainties on the transverse plane of 0.04% at r = 1 cm and 0.06% at r = 5 cm. Results: The dose-rate distribution ratio for the model S700 source as compared to the 2006 model exceeded unity by more than 5% for roughly one quarter of the solid angle surrounding the source, i.e., θ ≥ 120°. The radial dose function diminished in a similar manner as for an {sup 125}I seed, with values of 1.434, 0.636, 0.283, and 0.0975 at 0.5, 2, 5, and 10 cm, respectively. The radial dose function ratio between the current and the 2006 model had a minimum of 0.980 at 0.4 cm, close to the source sheath and for large distances approached 1.014. 2D anisotropy function ratios were close to unity for 50° ≤ θ ≤ 110°, but exceeded 5% for θ < 40° at close distances to the sheath and exceeded 15% for θ > 140°, even at large distances. Photon energy fluence of the updated model as compared to the 2006 model showed a decrease in output with increasing distance; this effect was pronounced at the lowest energies. A decrease in photon fluence with increase in polar angle was also observed and was attributed to the silver epoxy component. Conclusions: Changes in source design influenced the overall dose rate and distribution by more than 2% in several regions. This discrepancy is greater than the dose calculation acceptance criteria as recommended in the AAPM TG-56 report. The effect of the design change on the TG-43 parameters would likely not result in dose differences outside of patient applicators. Adoption of this new dataset is suggested for accurate depiction of model S700 source dose distributions.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

Computer-aided pulmonary image analysis in small animal models

Purpose: To develop an automated pulmonary image analysis framework for infectious lung diseases in small animal models. Methods: The authors describe a novel pathological lung and airway segmentation method for small animals. The proposed framework includes identification of abnormal imaging patterns pertaining to infectious lung diseases. First, the authors’ system estimates an expected lung volume by utilizing a regression function between total lung capacity and approximated rib cage volume. A significant difference between the expected lung volume and the initial lung segmentation indicates the presence of severe pathology, and invokes a machine learning based abnormal imaging pattern detection system next. The final stage of the proposed framework is the automatic extraction of airway tree for which new affinity relationships within the fuzzy connectedness image segmentation framework are proposed by combining Hessian and gray-scale morphological reconstruction filters. Results: 133 CT scans were collected from four different studies encompassing a wide spectrum of pulmonary abnormalities pertaining to two commonly used small animal models (ferret and rabbit). Sensitivity and specificity were greater than 90% for pathological lung segmentation (average dice similarity coefficient > 0.9). While qualitative visual assessments of airway tree extraction were performed by the participating expert radiologists, for quantitative evaluation the authors validated the proposed airway extraction method by using publicly available EXACT’09 data set. Conclusions: The authors developed a comprehensive computer-aided pulmonary image analysis framework for preclinical research applications. The proposed framework consists of automatic pathological lung segmentation and accurate airway tree extraction. The framework has high sensitivity and specificity; therefore, it can contribute advances in preclinical research in pulmonary diseases.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2023

Constraining the $\overline{K}$N coupled channel dynamics using femtoscopic correlations at the LHC

The interaction of K – with protons is characterised by the presence of several coupled channels, systems like $\overline{K}$ 0 n and πΣ with a similar mass and the same quantum numbers as the K – p state. The strengths of these couplings to the K – p system are of crucial importance for the understanding of the nature of the Λ(1405) resonance and of the attractive K – p strong interaction. In this article, we present measurements of the K – p correlation functions in relative momentum space obtained in pp collisions at $\sqrt{s}$=13 Te, in p–Pb collisions at $\sqrt{s_{NN}}$= 5.02 Te, and (semi)peripheral Pb–Pb collisions at $\sqrt{s_{NN}}$= 5.02 Te. The emitting source size, composed of a core radius anchored to the K + p correlation and of a resonance halo specific to each particle pair, varies between 1 and 2 fm in these collision systems. The strength and the effects of the $\overline{K}$ 0 n and πΣ inelastic channels on the measured K – p correlation function are investigated in the different colliding systems by comparing the data with state-of-the-art models of chiral potentials. A novel approach to determine the conversion weights ω, necessary to quantify the amount of produced inelastic channels in the correlation function, is presented. In this method, particle yields are estimated from thermal model predictions, and their kinematic distribution from blast-wave fits to measured data. The comparison of chiral potentials to the measured K – p interaction indicates that, while the πΣ –K – p dynamics is well reproduced by the model, the coupling to the $\overline{K}$ 0 n channel in the model is currently underestimated.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

Disproportionate impacts of COVID-19 in a large US city

COVID-19 has disproportionately impacted individuals depending on where they live and work, and based on their race, ethnicity, and socioeconomic status. Studies have documented catastrophic disparities at critical points throughout the pandemic, but have not yet systematically tracked their severity through time. Using anonymized hospitalization data from March 11, 2020 to June 1, 2021 and fine-grain infection hospitalization rates, we estimate the time-varying burden of COVID-19 by age group and ZIP code in Austin, Texas. During this 15-month period, we estimate an overall 23.7% (95% CrI: 22.5–24.8%) infection rate and 29.4% (95% CrI: 28.0–31.0%) case reporting rate. Individuals over 65 were less likely to be infected than younger age groups (11.2% [95% CrI: 10.3–12.0%] vs 25.1% [95% CrI: 23.7–26.4%]), but more likely to be hospitalized (1,965 per 100,000 vs 376 per 100,000) and have their infections reported (53% [95% CrI: 49–57%] vs 28% [95% CrI: 27–30%]). We used a mixed effect poisson regression model to estimate disparities in infection and reporting rates as a function of social vulnerability. We compared ZIP codes ranking in the 75th percentile of vulnerability to those in the 25th percentile, and found that the more vulnerable communities had 2.5 (95% CrI: 2.0–3.0) times the infection rate and only 70% (95% CrI: 60%-82%) the reporting rate compared to the less vulnerable communities. Inequality persisted but declined significantly over the 15-month study period. Our results suggest that further public health efforts are needed to mitigate local COVID-19 disparities and that the CDC’s social vulnerability index may serve as a reliable predictor of risk on a local scale when surveillance data are limited.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

Differentiable Predictions for Large Scale Structure with SHAMNet

In simulation-based models of the galaxy-halo connection, theoretical predictions for galaxy clustering and lensing are typically made based on Monte Carlo realizations of a mock universe. In this paper, we use Subhalo Abundance Matching (SHAM) as a toy model to introduce an alternative to stochastic predictions based on mock population, demonstrating how to make simulation-based predictions for clustering and lensing that are both exact and differentiable with respect to the parameters of the model. Conventional implementations of SHAM are based on iterative algorithms such as Richardson-Lucy deconvolution; here we use the JAX library for automatic differentiation to train SHAMNet, a neural network that accurately approximates the stellar-to-halo mass relation (SMHM) defined by abundance matching. In our approach to making differentiable predictions of large-scale structure, we map parameterized PDFs onto each simulated halo, and calculate gradients of summary statistics of the galaxy distribution by using autodiff to propagate the gradients of the SMHM through the statistical estimators used to measure one- and two-point functions. Our techniques are quite general, and we conclude with an overview of how they can be applied in tandem with more complex, higher-dimensional models, creating the capability to make differentiable predictions for the multi-wavelength universe of galaxies.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

Proton Dynamics Scenarios in the Integrable Optics Test Accelerator (IOTA) at Fermilab

The Integrable Optics Test Accelerator (IOTA) at Fermilab provides a versatile platform for studying the interplay of space-charge, impedance, and non-linear optics in high-intensity hadron beams within synchrotrons and storage rings. This report examines the parameters and dynamics of 2.5~MeV proton beam operations in two configurations of the bare IOTA lattice: one for demonstrating Non-linear Integrable Optics with the Danilov-Nagaitsev magnet, and the other for use with electron cooling. We offer order-of-magnitude estimates of the transverse emittance growth rate as a function of beam intensity, highlighting contributions from residual gas scattering, intra-beam scattering, and space-charge effects. Under nominal conditions, the beam lifetime is projected to be less than 7~minutes at low intensity with the current vacuum quality, and fewer than 100,000~turns at high intensity due to strong space-charge effects. The calculations presented here will guide strategies to mitigate emittance growth and inform future IOTA experiments.

43 PARTICLE ACCELERATORS↗

DOE OSTI2024

Reinforcement Learning Control for Enhancing Marine Hydrokinetic Turbine Energy Generation

This paper proposes a reinforcement learning-based method to maximize power generation for a direct-drive marine hydrokinetic turbine. A high levelized cost of energy (LCOE) is preventative in the widespread adoption of many marine energy conversion technologies. A straightforward way to reduce LCOE is to increase conversion efficiency and ensure maximum energy generation. The proposed method utilizes a damping control methodology, varying applied generator torque via a linear relationship between the applied damping coefficient and rotor speed. A state-action-reward-state-action (SARSA) algorithm has been used to learn the optimal control action for a given flow velocity. The proposed SARSA methodology uses Gaussian radial basis functions to create a three-dimensional surface to estimate the relationship between damping coefficient, incoming flow velocity, and coefficient of power (C p ). Here, the SARSA algorithm was compared against a baseline optimal tip speed ratio controller over a year-long flow velocity case profile while considering the effects of biofouling on the turbine system, where the proposed RL method generated 0.92% more energy than the baseline.

Damp↗