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All sources NASA NTRS DOE OSTI

DOE OSTI2016

WE-DE-201-01: BEST IN PHYSICS (THERAPY): A Fast Multi-Target Inverse Treatment Planning Strategy Optimizing Dosimetric Measures for High-Dose-Rate (HDR) Brachytherapy

Purpose: Inverse treatment planning (ITP) for interstitial HDR brachytherapy of gynecologic cancers seeks to maximize coverage of the clinical target volumes (tumor and vagina) while respecting dose-volume-histogram related dosimetric measures (DMs) for organs at risk (OARs). Commercially available ITP tools do not support DM-based planning because it is computationally too expensive to solve. In this study we present a novel approach that allows fast ITP for gynecologic cancers based on DMs for the first time. Methods: This novel strategy is an optimization model based on a smooth DM-based objective function. The smooth approximation is achieved by utilizing a logistic function for the evaluation of DMs. The resulting nonconvex and constrained optimization problem is then optimized with a BFGS algorithm. The model was evaluated using the implant geometry extracted from 20 patient treatment plans under an IRB-approved retrospective study. For each plan, the final DMs were evaluated and compared to the original clinical plans. The CTVs were the contoured tumor volume and the contoured surface of the vagina. Statistical significance was evaluated with a one-sided paired Wilcoxon signed-rank test. Results: As did the clinical plans, all generated plans fulfilled the defined DMs for OARs. The proposed strategy showed a statistically significant improvement (p<0.001) in coverage of the tumor and vagina, with absolute improvements of related DMs of (6.9 +/− 7.9)% and (28.2 +/− 12.0)%, respectively. This was achieved with a statistically significant (p<0.01) decrease of the high-dose-related DM for the tumor. The runtime of the optimization was (2.3 +/− 2.0) seconds. Conclusion: We demonstrated using clinical data that our novel approach allows rapid DM-based optimization with improved coverage of CTVs with fewer hot spots. Being up to three orders of magnitude faster than the current clinical practice, the method dramatically shortens planning time.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2017

Online Measurement of the Energy Spread of Multi-Turn Beam in the Fermilab Booster at Injection

We have developed a computer program interfaced with the ACNET environment of Fermilab accelerators to measure energy spread of the proton beam from the LINAC at an injection into the Booster. It uses a digitizing oscilloscope and provides users an ability to configure the scope settings for optimal data acquisition from a resistive wall current monitor. When the program is launched, a) a one shot timeline is generated to initiate beam injection into the Booster, b) a gap of about 40 ns is produced in the injected beam using a set of fast kickers, c) collects line charge distribution data from the wall current monitor for the first 200 μs from the injection and d) performs complete data analysis to extract full beam energy spread of the beam. The program also gives the option to store the data for offline analyses. We illustrate a case with an example. We also present results on beam energy spread as a function of beam intensity from recent measurements.

43 PARTICLE ACCELERATORS↗

DOE OSTI2023

Multimodal photon and neutron imaging devices with fast event-by-event multisampling readout and optimized particle type discrimination

A particle imaging method for distinguishing between types of incident particles, such as neutrons, photons, and alphas, and improving the position resolution of particle imaging devices with matrix readout. The method includes high frequency multisampling readout electronics that provides the sequences of multiple measurements for each detected event, resulting in recorded detailed waveform information describing the signals. Such detailed information is used to approximate each signal waveform with a parameterized function in which the extracted parameter sets determine the type of the incident particle in an optimized fashion. The detailed event-by-event multisampling information for each signal readout channel in the matrix readout of the radiation imaging devices improves and optimizes the position resolution for variable shapes of the signals. Such devices can be used in mixed radiation fields, creating a new class of multimodal photon and neutron imagers.

Degtiarenko, Pavel V.↗

DOE OSTI2017

Free energy functionals for polarization fluctuations: Pekar factor revisited

The separation of slow nuclear and fast electronic polarization in problems related to electron mobility in polarizable media was considered by Pekar 70 years ago. Within dielectric continuum models, this separation leads to the Pekar factor in the free energy of solvation by the nuclear degrees of freedom. The main qualitative prediction of Pekar’s perspective is a significant, by about a factor of two, drop of the nuclear solvation free energy compared to the total (electronic plus nuclear) free energy of solvation. The Pekar factor enters the solvent reorganization energy of electron transfer reactions and is a significant mechanistic parameter accounting for the solvent effect on electron transfer. Here, we study the separation of the fast and slow polarization modes in polar molecular liquids (polarizable dipolar liquids and polarizable water force fields) without relying on the continuum approximation. We derive the nonlocal free energy functional and use atomistic numerical simulations to obtain nonlocal, reciprocal space electronic and nuclear susceptibilities. A consistent transition to the continuum limit is introduced by extrapolating the results of finite-size numerical simulation to zero wavevector. The continuum nuclear susceptibility extracted from simulations is numerically close to the Pekar factor. However, we derive a new functionality involving the static and high-frequency dielectric constants. The main distinction of our approach from the traditional theories is found for the solvation free energy due to the nuclear polarization: the anticipated significant drop of its magnitude with increasing liquid polarizability does not occur. The reorganization energy of electron transfer is either nearly constant with increasing the solvent polarizability and the corresponding high-frequency dielectric constant (polarizable dipolar liquids) or actually noticeably increases (polarizable force fields of water).

14 SOLAR ENERGY↗

DOE OSTI2016

Data mining graphene: Correlative analysis of structure and electronic degrees of freedom in graphenic monolayers with defects

The link between changes in the material crystal structure and its mechanical, electronic, magnetic, and optical functionalities known as the structure-property relationship is the cornerstone of the contemporary materials science research. The recent advances in scanning transmission electron and scanning probe microscopies (STEM and SPM) have opened an unprecedented path towards examining the materials structure property relationships on the single-impurity and atomic-configuration levels. Lacking, however, are the statistics-based approaches for cross-correlation of structure and property variables obtained in different information channels of the STEM and SPM experiments. Here we have designed an approach based on a combination of sliding window Fast Fourier Transform, Pearson correlation matrix, linear and kernel canonical correlation, to study a relationship between lattice distortions and electron scattering from the SPM data on graphene with defects. Our analysis revealed that the strength of coupling to strain is altered between different scattering channels which can explain coexistence of several quasiparticle interference patterns in the nanoscale regions of interest. In addition, the application of the kernel functions allowed us extracting a non-linear component of the relationship between the lattice strain and scattering intensity in graphene. Lastly, the outlined approach can be further utilized to analyzing correlations in various multi-modal imaging techniques where the information of interest is spatially distributed and has usually a complex multidimensional nature.

36 MATERIALS SCIENCE↗

DOE OSTI2018

On the Early In Situ Formation of Pluto’s Small Satellites

The formation of Pluto’s small satellites—Styx, Nix, Keberos, and Hydra—remains a mystery. Their orbits are nearly circular and are near mean-motion resonances and nearly coplanar with Charon’s orbit. One scenario suggests that they all formed close to their current locations from a disk of debris that was ejected from the Charon-forming impact before the tidal evolution of Charon. The validity of this scenario is tested by performing N-body simulations with the small satellites treated as test particles and Pluto–Charon evolving tidally from an initial orbit at a few Pluto radii with initial eccentricity e {sub C} = 0 or 0.2. After tidal evolution, the free eccentricities e {sub free} of the test particles are extracted by applying fast Fourier transformation to the distance between the test particles and the center of mass of the system and compared with the current eccentricities of the four small satellites. The only surviving test particles with e {sub free} matching the eccentricities of the current satellites are those not affected by mean-motion resonances during the tidal evolution in a model with Pluto’s effective tidal dissipation function Q = 100 and an initial e {sub C} = 0.2 that is damped down rapidly. However, these test particles do not have any preference to be in or near 4:1, 5:1, and 6:1 resonances with Charon. An alternative scenario may be needed to explain the formation of Pluto’s small satellites.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2019

Ruprecht 147: A Paradigm of Dissolving Star Cluster

We employed recent Gaia/DR2 data to investigate the dynamical status of the nearby (300 pc), old (2.5 Gyr) open cluster Ruprecht 147. We found prominent leading and trailing tails of stars along the cluster orbit, which demonstrates that Ruprecht 147 is losing stars at fast pace. Star counts indicate the cluster has a core radius of 33.3 arcmin and a tidal radius of 137.5 arcmin. The cluster also possesses an extended corona, which cannot be reproduced by a simple King model. We computed the present-day cluster mass using its luminosity and mass function, and derived an estimate of 234 ± 52 M {sub ⊙}. We also estimated the cluster original mass using available recipes extracted from N-body simulations obtaining a mass at birth of 50,000 ± 6500 M {sub ⊙}. Therefore dynamical mass loss, mostly caused by tidal interaction with the Milky Way, reduced the cluster mass by about 99%. We then conclude that Ruprecht 147 is rapidly dissolving into the general Galactic disk.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2019

Genomic Signatures for Sedimentary Microbial Utilization of Phytoplankton Detritus in a Fast-Flowing Estuary

In fast-flowing, river-dominated estuaries, “hotspots” of microbial biogeochemical cycling can be found within areas of extended water retention. Lateral bays located off of the North and South channels of the Columbia River estuary are proposed to be such hotspots. Previous metagenomic studies on water samples indicated that these regions function both as sources and sinks of biogenic particles, with potential to impact organic matter fluxes in the estuary. To extend this work, we analyzed 11 sediment metagenomes from three disparate bays: the freshwater Cathlamet Bay, and the brackish Youngs Bay and more saline Baker Bay located nearer the mouth to the south and north of the main channel, respectively. Samples were collected from upper layers of sediments in August of 2011 and 2013 for DNA extraction and metagenome sequencing. All metagenomes were dominated by bacterial sequences, although diatom sequences as high as 26% of the total annotated sequences were observed in the higher salinity samples. Unsupervised 2D hierarchical clustering analysis resulted in the eleven metagenome samples clustered into four groups by microbial taxonomic composition, with Bacteroides, diatom, and phage levels driving most of the grouping. Results of functional gene clustering further indicated that diatom bloom degradation stage (early vs. late) was an important factor. While the Flavobacteriia and Cytophagia classes were well represented in metagenomes containing abundant diatoms, taxa from the Bacteroidia class, along with certain members of the Sphingobacteriia class, were particularly abundant in metagenomes representing later stages of diatom decomposition. In contrast, the sediment metagenomes with low relative abundance of diatom and Bacteroidetes sequences appeared to have a metabolic potential biased toward microbial growth under nutrient limitation. While differences in water salinity clearly also influenced the microbial community composition and metabolic potential, our results highlight a central role for allochthonous labile organic matter (i.e., diatom detritus), in shaping bacterial taxonomic and functional properties in the Columbia River estuary lateral bay sediments. These results suggest that in fast-flowing, river-dominated estuaries, sediment microbial communities in areas of extended water retention, such as the lateral bays, may contribute disproportionately to estuarine organic matter degradation and recycling.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2017

Quantum chemistry benchmarking of binding and selectivity for lanthanide extractants

Computer-aided screening methods facilitate the discovery of new extractants for heavy and rare-earth metal separations. In this work, we have benchmarked the accuracy of different quantum chemistry methods for calculating extractant binding energies and selectivities. Specifically, we compare calculated data from different exchange correlation functionals (B3LYP-D3, ωB97X-D3, and M06-L) and different basis sets (including large-core effective core potentials and all-electron basis sets). We report aqueous-phase binding energy and selectivity trends for 1:1 and 3:1 extractant/lanthanide models for the complexes. We find that binding selectivities are not particularly sensitive to model chemistry, but binding energies are sensitive. Furthermore, calculated trends in selectivity using 3:1 extractant/lanthanide models are in better agreement with available experimental trends than trends using 1:1 extractant/lanthanide models. Lastly, we find that the B3LYP-D3/6-31 + G* model chemistry with the Stuttgart large-core relativistic effective core potentials on the lanthanide sufficiently reproduces results from larger basis set calculations and is confirmed as suitable for relatively fast and efficient screening of lanthanide binding energies and selectivities.

36 MATERIALS SCIENCE↗

DOE OSTI2024

A deep learning-based workflow for fast prediction of 3D state variables in geological carbon storage: A dimension reduction approach

Deep learning (DL) models are extensively used as surrogate models for high-fidelity simulations of multiphase fluid flow in porous media at large scales, enabling fast forecasts of the spatial–temporal evolution of three-dimensional (3D) state variables in geological carbon storage (GCS). However, training these models in high-dimensional space remains computationally demanding and prone to overfitting because of limited training data. This paper presents a novel workflow to address these challenges by integrating dimension reduction (DR) methods. Here, the proposed workflow employed pre-trained DR models to extract the latent variables of geological models and state variables and utilized the multi-layer perceptron (MLP) for constructing mapping functions between the input and output variables in latent spaces. Subsequently, the pre-trained reconstruction models converted the MLP-predicted latent state variables to their original high-dimensional form. Furthermore, we proposed a novel strategy for the DR and reconstruction of 3D saturation fields to account for the unique data characteristics of sparsity, nonuniformity, and discontinuity. The proposed strategy applied PCA and inverse PCA for 2D average saturation fields and developed a DL-based 3D reconstruction model, leveraging three 2D average saturation fields as input to produce a 3D saturation field as output. The pre-training of DR and reconstruction models and training of MLP models were conducted on 84 Gulf of Mexico (GoM) simulations and evaluated on 12 testing simulations. Each simulation contained 720 monthly time steps, with the first 360 months as the injection period and the rest as the post-injection period. The proposed workflow, incorporating DR and DL models, accurately predicts the normalized 3D pressure fields, achieving mean square error (MSE) of 2.92 × 10 -7 compared to the ground truth obtained from a full-physics simulator. Furthermore, the proposed strategy outperformed PCA and convolutional autoencoder (CAE) models on 3D saturation fields, resulting in minor workflow prediction errors with an MSE of 2.93 × 10 -5 . The results suggest the proposed workflow provides sufficient predictive fidelity across temporal and spatial scales, and enables a speedup of 160 times compared to the full-physics simulator, facilitating improved decision-making and risk assessment for large-scale GCS management in real-time scenarios.

3D reconstruction model↗

DOE OSTI2022

Efficient machine-learning model for fast assessment of elastic properties of high-entropy alloys

We combined descriptor-based analytical models for stiffness-matrix and elastic-moduli with mean-field methods to accelerate assessment of technologically useful properties of high-entropy alloys, such as strength and ductility. Model training for elastic properties uses Sure-Independence Screening (SIS) and Sparsifying Operator (SO) method yielding an optimal analytical model, constructed with meaningful atomic features to predict target properties. Computationally inexpensive analytical descriptors were trained using a database of elastic properties determined from density functional theory for binary and ternary subsets of Nb-Mo-Ta-W-V refractory alloys. The optimal Elastic-SISSO models, extracted from an exponentially large feature space, give an extremely accurate prediction of target properties, similar to or better than other models, with some verified from existing experiments. Here we also show that electronegativity variance and elastic-moduli can directly predict trends in ductility and yield strength of refractory HEAs, and reveals promising alloy concentration regions.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Modeling the co-assembly of binary nanoparticles

Abstract In this work, we present a binary assembly model that can predict the co-assembly structure and spatial frequency spectra of monodispersed nanoparticles with two different particle sizes. The approach relies on an iterative algorithm based on geometric constraints, which can simulate the assembly patterns of particles with two distinct diameters, size distributions, and at various mixture ratios on a planar surface. The two-dimensional spatial-frequency spectra of the modeled assembles can be analyzed using fast Fourier transform analysis to examine their frequency content. The simulated co-assembly structures and spectra are compared with assembled nanoparticles fabricated using transfer coating method are in qualitative agreement with the experimental results. The co-assembly model can also be used to predict the peak spatial frequency and the full-width at half-maximum bandwidth, which can lead to the design of the structure spectra by selection of different monodispersed particles. This work can find applications in fabrication of non-periodic nanostructures for functional surfaces, light extraction structures, and broadband nanophotonics.

Mohanty, Saurav (ORCID:0009000302592855)↗

DOE OSTI2022

Ionic Control over Ferroelectricity in 2D Layered van der Waals Capacitors

The van der Waals layered material CuInP 2 S 6 features interesting functional behavior, including the existence of four uniaxial polarization states, polarization reversal against the electric field through Cu ion migration, a negative-capacitance regime, and reversible extraction of Cu ions. At the heart of these characteristics lies the high mobility of Cu ions, which also determines the spontaneous polarization. Therefore, Cu migration across the lattice results in unusual ferroelectric behavior. Here, we demonstrate how the interplay of polar and ionic properties provides a path to ionically controlled ferroelectric behavior, achieved by applying selected DC voltage pulses and subsequently probing ferroelectric switching during fast triangular voltage sweeps. Using current measurements and theoretical calculations, we observe that increasing DC pulse duration results in higher ionic currents, the buildup of an internal electric field that shifts polarization loops, and an increase in total switchable polarization by ~50% due to the existence of a high polarization phase which is stabilized by the internal electric field. Apart from tuning ferroelectric behavior by selected square pulses, hysteretic polarization switching can even be entirely deactivated and reactivated, resulting in three-state systems where polarization switching is either inhibited or can be performed in two different directions.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Graph Contractions for Calculating Correlation Functions in Lattice QCD

Computing correlation functions for many-particle systems in Lattice QCD is vital to extract nuclear physics observables like the energy spectrum of hadrons such as protons. However, this type of calculation has long been considered to be very challenging and computing-resource intensive because of the complex nature of a hadron composed of quarks with many degrees of freedom. In particular, a correlation function can be calculated through a sum of all possible pairs of quark contractions, each of which is a batched tensor contraction, dictated by Wick's theorem. Because the number of terms of this sum can be very large for any hadronic system of interest, fast evaluation of the sum faces several challenges: an extremely large number of contractions, a huge memory footprint at runtime, and the speed of tensor contractions. In this paper, we present a Lattice QCD analysis software suite, Redstar, which addresses these challenges by utilizing novel algorithmic and software engineering methods targeting modern computing platforms such as many-core CPUs and GPUs. In particular, Redstar represents every term in the sum of a correlation function by a graph, applies efficient graph algorithms to reduce the number of contractions to lower the cost of computations, and minimizes the total memory footprint. Moreover, Redstar carries out the contractions on either CPUs or GPUs utilizing an internal and highly efficient Hadron contraction library. Specifically, we illustrate some important algorithmic optimizations of Redstar, show various key design features of Hadron library, and present the speedup values due to the optimizations along with performance figures for calculating six correlations functions on four computing platforms.

Chen, Jie↗

DOE OSTI2023

Graph Contractions for Calculating Correlation Functions in Lattice QCD

Chen, Jie↗

DOE OSTI2024

Dimensionally Aligned Signal Projection Algorithms Library

Dimensionally aligned signal projection (DASP) algorithms are used to analyze fast Fourier transforms (FFTs) and generate visualizations that help focus on the harmonics for specific signals. At a high level, these algorithms extract the FFT segments around each harmonic frequency center, and then align them in equally sized arrays ordered by increasing distance from the base frequency. This allows for a focused view of the harmonic frequencies, which, among other use cases, can enable machine learning algorithms to more easily identify salient patterns. This work seeks to provide an effective open-source implementation of the DASP algorithms proposed by Vann et al. (2018) as well as functionality to help explore and test how these algorithms work with an interactive dashboard and signal-generation tool. The DASP library is implemented in Python and contains four types of algorithms for implementing these feature engineering techniques: fixed harmonically aligned signal projection (HASP), decimating HASP, interpolating HASP, and frequency aligned signal projection (FASP). Each algorithm returns a numerical array, which can be visualized as an image. The HASP algorithms are variations of the algorithms originally presented by Vann et al. (2018). For consistency, FASP, which is the terminology used for the short-time Fourier transform (STFT), has been implemented as part of the library to provide a similar interface to the STFT of the raw signal. Additionally, the library contains an algorithm to generate artificial signals with basic customizations such as the base frequency, sample rate, duration, number of harmonics, noise, and number of signals. Finally, the library provides multiple interactive visualizations, each of which is implemented using IPyWidgets and works in a Jupyter environment. A dashboard-style visualization is provided, which contains some common signal-processing visual components (signal, FFT, spectogram) updating in unison with the HASP functions (see Figure 1 below). Separate from the dashboard, an independent visualization is provided for each of the DASP algorithms as well as for the artifical signal generator. These visualizations are included in the library to aid in developing an intuitive understanding how the algorithms are affected by different input signals and parameter selections.

harmonics↗

DOE OSTI2022

Determination of Molecular Structure and Dynamics of Molten Salts by Advanced Neutron and X-ray Scattering Measurements and Computer Modeling

The design and development of fully functional Molten-Salt Reactors (MSR) require detailed knowledge of the molten salt properties in order to understand and predict the salt’s behavior. Fundamental properties of interest include molecular structure, speciation, and dynamics (such as diffusion coefficients) of salt components and dissolved corrosion and fission products. Computer modeling is necessary to predict changes in physical and chemical properties due to irradiation, burning of dissolved fuel, and corrosion. The modeling requires experimental data, and advanced neutron and x-ray scattering and spectroscopy provide the most reliable and direct determination of the structure (Pair-Distribution Functions, PDF), and dynamics of ions in the melt. This project dealt with both fluoride and chloride salts. The PDFs have been measured by a combination of neutron and x-ray diffraction. We utilized the techniques of isotope substitutions, a very powerful tool available for neutron-scattering, to extract the details of the liquid structure. Although similar measurements have been done before, modern advanced neutron and x-ray-scattering techniques allow collecting the data at much higher resolution and in a wider range of temperatures. Importantly, we were among the first to study fluoride salts by neutron scattering. The importance of impurities and their effects on salt properties have become apparent recently and so new methods of salt purification were developed. We took advantage of these developments to produce reliable data, which have been used for computer simulations of both clean salts and those with added fission and corrosion products most relevant for MSRs. Ab initio molecular dynamics simulations have been performed to understand the multi-component liquid solution, in particular solubility of impurities and thermodynamic interactions in relation to the ionic-cluster structure of the fluid. We applied machine learning to regress from the simulation and experimental data in order to develop a fast-acting model that can handle molten salt with an arbitrary (≥ 10) number of chemical elements and be able to predict chemical potential as a function of composition and temperature. This project resulted in a number of experimental and computer-simulation publications, a patent application, and numerous conference presentations (American Physical Society, American Chemical Society, and The Electrochemical Society among others). Multiple students and postdocs participated and collaborated on aspects of this project. This project seeded new collaborations between MIT and other institutions, such as the University of Massachusetts Lowell, the University of Illinois Urbana-Champaign, the University of California Berkeley, and Oak Ridge and Los Alamos National Labs. As such, this project has had a broad and lasting impact beyond its original scientific scope.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2025

Fast HARDI Uncertainty Quantification and Visualization with Spherical Sampling

In this paper, we study uncertainty quantification and visualization of orientation distribution functions (ODF), which corresponds to the diffusion profile of high angular resolution diffusion imaging (HARDI) data. The shape inclusion probability (SIP) function is the state‐of‐the‐art method for capturing the uncertainty of ODF ensembles. The current method of computing the SIP function with a volumetric basis exhibits high computational and memory costs, which can be a bottleneck to integrating uncertainty into HARDI visualization techniques and tools. We propose a novel spherical sampling framework for faster computation of the SIP function with lower memory usage and increased accuracy. In particular, we propose direct extraction of SIP isosurfaces, which represent confidence intervals indicating spatial uncertainty of HARDI glyphs, by performing spherical sampling of ODFs. Our spherical sampling approach requires much less sampling than the state‐of‐the‐art volume sampling method, thus providing significantly enhanced performance, scalability, and the ability to perform implicit ray tracing. Our experiments demonstrate that the SIP isosurfaces extracted with our spherical sampling approach can achieve up to 8164× speedup, 37282× memory reduction, and 50.2% less SIP isosurface error compared to the classical volume sampling approach. We demonstrate the efficacy of our methods through experiments on synthetic and human‐brain HARDI datasets.

97 MATHEMATICS AND COMPUTING↗