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DOE OSTI2023

Barrier Heights for Diels–Alder Transition States Leading to Pentacyclic Adducts: A Benchmark Study of Crowded, Strained Transition States of Large Molecules

Theoretical characterization of reactions of complex molecules depends on providing consistent accuracy for the relative energies of intermediates and transition states. Here we employ the DLPNO-CCSD(T) method with core–valence correlation, large basis sets, and extrapolation to the CBS limit to provide benchmark values for Diels–Alder transition states leading to competitive strained pentacyclic adducts. We then used those benchmarks to test a diverse set of wave function and density functional methods for the absolute and relative barrier heights of these transition states. Our results show that only a few of the tested density functionals can predict the absolute barrier heights satisfactorily, although relative barrier heights are more accurate. The most accurate functionals tested are ωB97M-V, M11plus, ωB97X-V, PBE-D3(0), M11, and MN15 with MUDs from best estimates less than 3.0 kcal. Furthermore, these findings can guide selection of density functionals for future studies of crowded, strained transition states of large molecules.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Inclusive reactions from finite Minkowski spacetime correlation functions

The need to determine scattering amplitudes of few-hadron systems for arbitrary kinematics expands a broad set of subfields of modern-day nuclear and hadronic physics. In this work, we expand upon previous explorations on the use of real-time methods, like quantum computing or tensor networks, to determine few-body scattering amplitudes. Such calculations must be performed in a finite Minkowski spacetime, where scattering amplitudes are not well defined. Our previous work presented a conjecture of a systematically improvable estimator for scattering amplitudes constructed from finite-volume correlation functions. Here we provide further evidence that the prescription works for larger kinematic regions than previously explored as well as a broader class of scattering amplitudes. Finally, we devise a new method for estimating the order of magnitude of the error associated with finite time separations needed for such calculations. In units of the lightest mass of the theory, we find that to constrain amplitudes using real-time methods within O ( 10 % ) , the spacetime volumes must satisfy m L ∼ O ( 10 – 10 2 ) ) and m T ∼ O ( 10 2 – 10 4 ) . Published by the American Physical Society 2024

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

Lanczos algorithm for lattice QCD matrix elements

Recent work [M. L. Wagman, Lanczos, the transfer matrix, and the signal-to-noise problem, .] found that an analysis formalism based on the Lanczos algorithm allows energy levels to be extracted from Euclidean correlation functions with faster ground-state convergence than effective masses, convergent estimators for multiple states from a single correlator, and two-sided error bounds. After filtering out spurious eigenvalues and using outlier-robust estimators within a nested bootstrap framework, Lanczos estimators behave more like multistate fit results than effective masses—but without involving statistical fitting. We extend this formalism to the determination of matrix elements from three-point correlation functions and provide a physical picture of “spurious-state filtering” involving restriction to a Hermitian subspace. We demonstrate similar advantages for matrix elements as for spectroscopy through example applications to noiseless mock-data and (bare) forward matrix elements of the strange scalar current between both ground and excited states with the quantum numbers of the nucleon.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2022

Unraveling the Wrinkle in Time-Variable Sources with Lunes and Synthetic Seismic Data

In this report, we describe how to estimate the time-variable components of the seismic moment tensor and compare these estimates to the more conventional analysis that incorporates an assumption of the source time function (STF) across all components of the seismic moment tensor. The advantage of our method is that we are able to independently estimate the time-evolution of each component of the seismic moment tensor, which may help to resolve the complex source phenomena associated with buried explosions. By performing an eigen decomposition of the time-evolving seismic moment tensor components, we are able to plot the seismic mechanism as a trajectory on a lune diagram. This technique enables interpretation of the seismic mechanism as a function of time, as opposed to the more conventional analysis which assumes that the seismic mechanism is time invariant. Finally, we describe the differences between the seismic moment and the seismic moment rate STFs, how to implement each one in inversion schemes, and the relative strengths/weaknesses of each. Our key take-away is that we are able to distinguish nearly-overlapping sources with highly different mechanisms, such as an explosion immediately following an earthquake, by estimating moment rate from seismic data through a STF-invariant inversion for the full time-variable moment tensor.

58 GEOSCIENCES↗

DOE OSTI2023

New machine learning techniques for simulation-based inference: InferoStatic nets, kernel score estimation, and kernel likelihood ratio estimation

We propose an intuitive, machine-learning approach to multiparameter inference, dubbed the InferoStatic Networks (ISN) method, to model the score and likelihood ratio estimators in cases when the probability density can be sampled but not computed directly. The ISN uses a backend neural network that models a scalar function called the inferostatic potential \varphi φ . In addition, we introduce new strategies, respectively called Kernel Score Estimation (KSE) and Kernel Likelihood Ratio Estimation (KLRE), to learn the score and the likelihood ratio functions from simulated data. We illustrate the new techniques with some toy examples and compare to existing approaches in the literature. We mention en passant some new loss functions that optimally incorporate latent information from simulations into the training procedure.

Kong, Kyoungchul↗

DOE OSTI2023

Codebase release 0.1 for infstat

Kong, Kyoungchul↗

DOE OSTI2022

Monitoring Seasonal Shear Wave Velocity Changes in the Top 6 m at Garner Valley in Southern California With Borehole Data

Abstract Subsurface structures play important roles in seismic ground motion, crustal hydrology, stability of the built environment, and more. Constraining temporal changes of subsurface shear wave velocity ( V S ) can provide useful information to all these topics and the growing field of hydrological monitoring with seismic velocity. Using borehole records at Garner Valley, CA, we estimate seasonal subsurface V S variations from impulse response functions (IRFs) of earthquake data (2005–2018) along with IRFs and cross‐correlation of cross‐hole experiment data (2015–2018). The inferred V S variations are up to ∼25% in the top 6 m and ∼10% at 2–5 m in depth. The V S variations correlate strongly with the water table depth changes, suggesting that the changes are mostly due to fluctuations of pore pressure in the shallow material. The shallow velocity changes alter the near‐surface conditions, can affect seismic hazard estimation, and may be improperly attributed to deeper processes without careful analysis.

58 GEOSCIENCES↗

DOE OSTI2024

A Nonlinear Least Squares Phasor Estimation Algorithm with a Trust Metric

The paper presents a separable nonlinear least squares (NLLS)-based approach for estimation of fundamental-frequency phasors from sampled point-on-wave measurements. An analytical connection is established between the NLLS cost function and the discrete Fourier transform (DFT)-based periodogram of the input signal. This periodogram-based interpretation of the cost function offers an intuitive and easy-to-implement solution for the frequency estimate. Using the residual error of the NLLS-fit, the paper also presents an insightful measure for ascertaining the quality of the phasor estimates and their validity, especially for data windows containing signal transients.

Chatterjee, Kaustav↗

DOE OSTI2018

Elastic functional principal component regression

We study regression using functional predictors in situations where these functions contains both phase and amplitude variability. In other words, the functions are misaligned due to errors in time measurements, and these errors can significantly degrade both model estimation and prediction performance. The current techniques either ignore the phase variability, or handle it via preprocessing, that is, use an off–the–shelf technique for functional alignment and phase removal. We develop a functional principal component regression model which has a comprehensive approach in handling phase and amplitude variability. The model utilizes a mathematical representation of the data known as the square–root slope function. These functions preserve the L 2 norm under warping and are ideally suited for simultaneous estimation of regression and warping parameters. Furthermore, using both simulated and real–world data sets, we demonstrate our approach and evaluate its prediction performance relative to current models. In addition, we propose an extension to functional logistic and multinomial logistic regression.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

Electric train energy consumption modeling

For this paper we develop an electric train energy consumption modeling framework considering instantaneous regenerative braking efficiency in support of a rail simulation system. The model is calibrated with data from Portland, Oregon using an unconstrained non-linear optimization procedure, and validated using data from Chicago, Illinois by comparing model predictions against the National Transit Database (NTD) estimates. The results demonstrate that regenerative braking efficiency varies as an exponential function of the deceleration level, rather than an average constant as assumed in previous studies. The model predictions are demonstrated to be consistent with the NTD estimates, producing a predicted error of 1.87% and -2.31%. The paper demonstrates that energy recovery reduces the overall power consumption by 20% for the tested Chicago route. Furthermore, the paper demonstrates that the proposed modeling approach is able to capture energy consumption differences associated with train, route and operational parameters, and thus is applicable for project-level analysis. The model can be easily implemented in traffic simulation software, used in smartphone applications and eco-transit programs given its fast execution time and easy integration in complex frameworks.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2018

An atomistic investigation of the interaction of dislocations with Guinier-Preston zones in Al-Cu alloys

The interaction between edge dislocations and Guinier-Preston zones in an Al-Cu alloy was analyzed by means of atomistic simulations. The different thermodynamic functions that determine the features of these obstacles for the dislocation glide were computed using molecular statics, molecular dynamics and the nudged elastic band method. It was found that Guinier-Preston zones are sheared by dislocations and the rate at which dislocations overcome the precipitate is controlled by the activation energy, Δ U , in agreement with the postulates of the harmonic transition state theory. Moreover, the entropic contribution to the Helmholtz activation free energy was in the range 1.3–1.8 k b , which can be associated with the typical vibrational entropy of solids. Finally, an estimation of the initial shear flow stress as a function of temperature was carried out from the thermodynamic data provided by the atomistic simulations. Furthermore, comparison with experimental results showed that the effect of the random precipitate distribution and of the dislocation character and dislocation/precipitation orientation has to be taken into account in the simulations to better reproduce experiments.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Impedance-Based Characterization of Power System Frequency Response: Preprint

This paper presents an impedance-based noninvasive approach for the characterization of power system frequency response in real time in the absence of a transient event. It measures transfer function response from the injected active power to the frequency at the point of interconnection for the estimation of inertia and primary frequency response of the system. The so-called frequency response function also shows the effects of the speed of primary frequency control. The paper also derives the relationship between the frequency response function and the network impedance seen from the point of interconnection. This relationship highlights how impedance captures the frequency response behavior of a network. The proposed methodology is demonstrated on a modified IEEE 9-bus system with 25% penetration of wind and PV generation; a 5-MW battery energy storage system is used for the injection of active power perturbations for the measurement of the frequency response function.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

Evaluating Gaussian process metamodels and sequential designs for noisy level set estimation

Abstract We consider the problem of learning the level set for which a noisy black-box function exceeds a given threshold. To efficiently reconstruct the level set, we investigate Gaussian process (GP) metamodels. Our focus is on strongly stochastic simulators, in particular with heavy-tailed simulation noise and low signal-to-noise ratio. To guard against noise misspecification, we assess the performance of three variants: (i) GPs with Student- t observations; (ii) Student- t processes (TPs); and (iii) classification GPs modeling the sign of the response. In conjunction with these metamodels, we analyze several acquisition functions for guiding the sequential experimental designs, extending existing stepwise uncertainty reduction criteria to the stochastic contour-finding context. This also motivates our development of (approximate) updating formulas to efficiently compute such acquisition functions. Our schemes are benchmarked by using a variety of synthetic experiments in 1–6 dimensions. We also consider an application of level set estimation for determining the optimal exercise policy of Bermudan options in finance.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Inclusive and multiplicity dependent production of electrons from heavy-flavour hadron decays in pp and p-Pb collisions

Measurements of the production of electrons from heavy-flavour hadron decays in pp collisions at $\sqrt{s}$ = 13 TeV at midrapidity with the ALICE detector are presented down to a transverse momentum (p T ) of 0.2 GeV/c and up to p T = 35 GeV/c, which is the largest momentum range probed for inclusive electron measurements in ALICE. In p-Pb collisions, the production cross section and the nuclear modification factor of electrons from heavy-flavour hadron decays are measured in the p T range 0.5 < p T < 26 GeV/c at $\sqrt{s_{NN}}$= 8.16 TeV. The nuclear modification factor is found to be consistent with unity within the statistical and systematic uncertainties. In both collision systems, first measurements of the yields of electrons from heavy-flavour hadron decays in different multiplicity intervals normalised to the multiplicity-integrated yield (self-normalised yield) at midrapidity are reported as a function of the self-normalised charged-particle multiplicity estimated at midrapidity. The self-normalised yields in pp and p-Pb collisions grow faster than linear with the self-normalised multiplicity. A strong p T dependence is observed in pp collisions, where the yield of high-p T electrons increases faster as a function of multiplicity than the one of low-p T electrons. The measurement in p-Pb collisions shows no p T dependence within uncertainties. The self-normalised yields in pp and p-Pb collisions are compared with measurements of other heavy-flavour, light-flavour, and strange particles, and with Monte Carlo simulations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Simple self-consistent method for excited states in density functional theory to characterize defect-derived behavior in wide-band-gap-based microelectronic materials

This final report summarizes the results of the Laboratory Direct Research and Development (LDRD) Project Number 229740. Wide band gap semiconductors such as gallium nitride (GaN) have features highly desirable for multiple mission electronic applications. Realization of their potential requires atomic-scale understanding of electronic behavior. The principal experimental tools for electronically probing defects in GaN are chemically undifferentiating and lack a practical theoretical counterpart needed to identify and characterize specific defects. This project investigated whether a simple idea for modeling defect excited states and their associated photoluminescence (PL) energies is viable, as a path to accelerate the understanding of defect behavior and gain valuable insights into engineering new electronic materials and devices. The research implemented a non-self-consistent total-energy evaluation of a Koopmans-type estimation of an excited electronic state energy in density functional theory (DFT) calculations, and proceeded to design, implement, and assess a self-consistent method for computing excited states based upon an OCcupation-Constrained-DFT (occ-DFT). The occ-DFT was verified in test calculations of defect excited states and validated against well-characterized PL data for 3d transition metal defects in GaN. The method proved stable and robust in computing excited states and gave accurate predictions compared to experimental PL data. The combined ground state/excited-state capability proved capable of chemically differentiating defect species in GaN. In application to 3d dopants in GaN, we reinterpreted extensive experimental literature, proposed new defects as prospective candidates for use in quantum information applications, and outlined design strategies to create and exploit these potentially useful functional defects in GaN.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Leveraging transfer learning and leaf spectroscopy for leaf trait prediction with broad spatial, species, and temporal applicability

Accurate and reliable prediction of leaf traits is crucial for understanding plant adaptations to environmental variation, monitoring terrestrial ecosystems, and enhancing comprehension of functional diversity and ecosystem functioning. Currently, various approaches (e.g., statistical, physical models) have been developed to estimate leaf traits through hyperspectral remote sensing and leaf spectroscopy. However, the absence of high-performing, transferable, and stable models across various domains of space, plant functional types (PFTs) and seasons hinder our ability to quantify and comprehend spatiotemporal variations in leaf traits. This study proposes robust and highly transferable models for better predicting leaf traits with hyperspectral reflectance. Initially, three datasets were assembled, pairing common leaf traits — chlorophyll (Chla+b), carotenoids (Ccar), leaf mass per area (LAM), equivalent water thickness (EWT) — with leaf spectra measurements collected across diverse geographic locations in the U.S. and Europe, PFTs, and seasons. Measurements were acquired using spectroradiometers (e.g., ASD FieldSpec 3/4/Pro and SVC HR-1024i) with integrating spheres, leaf clips, and contact probes. Here, we then developed transfer learning-based hybrid models that incorporated the domain knowledge of radiative transfer models (RTMs) through pretraining processes and were well-constrained by fine-tuning with field measurements. Through comparison with other state-of-the-art statistical models, including partial-least squares regression (PLSR) and Gaussian Process Regression (GPR), as well as pure physical models, we found that the proposed transfer learning models achieved better predictive performance and higher transferability. Specifically, compared to other statistical models and pure RTMs, the transfer learning model exhibited higher coefficient of determination (R 2 ) values with range of 0.01 to 0.79, lower normalized root mean square error (NRMSE) with range of 0.06 % to 33.25 % in model performance. Additionally, the models exhibited improved transferability, with higher R 2 values range from 0.04 to 0.32, lower NRMSE range from 0.08 % to 30.81 %. The findings underscore that transfer learning models through integrating domain knowledge from RTMs and limited observations, can harness the advantages of both RTMs and statistical models and serve as a promising approach for effectively predicting leaf traits.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Impact of acute temperature and air pollution exposures on adult lung function: A panel study of asthmatics

Background Individuals with respiratory conditions, such as asthma, are particularly susceptible to adverse health effects associated with higher levels of ambient air pollution and temperature. This study evaluates whether hourly levels of fine particulate matter (PM 2.5 ) and dry bulb globe temperature (DBGT) are associated with the lung function of adult participants with asthma. Methods and findings Global positioning system (GPS) location, respiratory function (measured as forced expiratory volume at 1 second (FEV 1 )), and self-reports of asthma medication usage and symptoms were collected as part of the Exposure, Location, and Lung Function (ELF) study. Hourly ambient PM 2.5 and DBGT exposures were estimated by integrating air quality and temperature public records with time-activity patterns using GPS coordinates for each participant (n = 35). The relationships between acute PM 2.5 , DBGT, rescue bronchodilator use, and lung function collected in one week periods and over two seasons (summer/winter) were analyzed by multivariate regression, using different exposure time frames. In separate models, increasing levels in PM 2.5 , but not DBGT, were associated with rescue bronchodilator use. Conversely DBGT, but not PM 2.5 , had a significant association with FEV 1 . When DBGT and PM 2.5 exposures were placed in the same model, the strongest association between cumulative PM 2.5 exposures and the use of rescue bronchodilator was identified at the 0–24 hours (OR = 1.030; 95% CI = 1.012–1.049; p-value = 0.001) and 0–48 hours (OR = 1.030; 95% CI = 1.013–1.057; p-value = 0.001) prior to lung function measure. Conversely, DBGT exposure at 0 hours (β = 3.257; SE = 0.879; p-value>0.001) and 0–6 hours (β = 2.885; SE = 0.903; p-value = 0.001) hours before a reading were associated with FEV 1 . No significant interactions between DBGT and PM 2.5 were observed for rescue bronchodilator use or FEV 1 . Conclusions Short-term increases in PM 2.5 were associated with increased rescue bronchodilator use, while DBGT was associated with higher lung function (i.e. FEV 1 ). Further studies are needed to continue to elucidate the mechanisms of acute exposure to PM 2.5 and DBGT on lung function in asthmatics.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2020

Binding Affinity Prediction by Pairwise Function Based on Neural Network

In this paper, we present a new approach to estimate the binding affinity from given three-dimensional poses of protein–ligand complexes. In this scheme, every protein–ligand atom pair makes an additive free-energy contribution. The sum of these pairwise contributions then gives the total binding free energy or the logarithm of the dissociation constant. The pairwise contribution is calculated by a function implemented via a neural network that takes the properties of the two atoms and their distance as input. The pairwise function is trained using a portion of the PDBbind 2018 data set. The model achieves good accuracy for affinity predictions when evaluated with PDBbind 2018 and with the CASF-2016 benchmark, comparing favorably to many scoring functions such as that of AutoDock Vina. The framework here may be extended to incorporate other factors to further improve its accuracy and power.

59 BASIC BIOLOGICAL SCIENCES↗