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DOE OSTI2020

Quantal diffusion approach for multinucleon transfer processes in the 58,64 Ni + 208 Pb reactions: Toward the production of unknown neutron-rich nuclei

In recent years, substantial efforts have been made for the study of multinucleon transfer reactions at energies around the Coulomb barrier both experimentally and theoretically, aiming at the production of unknown neutron-rich heavy nuclei. It is crucial to provide reliable theoretical predictions based on microscopic theories with sufficient predictive power. Purpose: This article aims to clarify the applicability of the quantal diffusion approach based on the stochastic mean-field (SMF) theory for multinucleon transfer processes. Isotope production cross sections are evaluated for the reactions of 64 Ni + 208 Pb at E c.m. = 268 MeV and 58 Ni + 208 Pb at E c.m. = 270 MeV and are compared with available experimental data. Methods: Three-dimensional time-dependent Hartree-Fock (TDHF) calculations are carried out for a range of initial orbital angular momenta with Skyrme SLy4d functional. Quantal diffusion equations, derived based on the SMF theory, for variances and covariance of neutron and proton numbers of reaction products are solved, with microscopic drift and diffusion coefficients obtained from time evolution of occupied single-particle orbitals in TDHF. Secondary de-excitation processes, both particle evaporation and fission, are simulated by a statistical compound-nucleus de-excitation model, gemini++. Dynamics of a fast isospin equilibration process followed by a slow drift toward the mass symmetry are commonly observed, as expected. Various reaction outcomes are evaluated, including average mass and charge numbers of reaction products, total kinetic energy loss (TKEL), scattering angle, contact time, and production cross sections for primary and secondary products. By comparing with the experimental data, we find that SMF and TDHF quantitatively reproduce experimental data for few-nucleon-transfer channels around the average values. In contrast, for many-nucleon-transfer channels, we find that the SMF approach provides much better description of the experimentally measured isotopic distributions. The results underline the importance of beyond-mean-field effects, especially one-body (mean-field) fluctuations and correlations, in describing multinucleon transfer processes. Moreover, through a combined analysis of SMF with a statistical model, gemini++, we find a significant contribution of transfer-induced fission, which is consistent with the experimental observation. In some cases, the SMF approach overestimates the isotopic width, requiring further improvements of the theoretical description. Possible ways to improve the description are discussed. The SMF approach is designed to describe the quantum many-body problem according to an ensemble of mean-field trajectories, taking into account part of many-body correlations in the description. As it requires feasible computational costs comparable to the ordinary TDHF approach, together with further model improvements, it will be a promising tool in the search for optimal reaction conditions to produce yet-unknown neutron-rich heavy nuclei through the multinucleon transfer reaction.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2020

An analytical expression for temperature in a thermodynamically consistent model with a Mie-Gruneisen equation for pressure

The objective of this paper is to develop an analytical expression for temperature in a thermodynamically consistent model with a Mie-Gruneisen equation for pressure. This expression for the temperature is obtained for both constant and variable specific heat models and can be used in common models for the material yield strength that include thermal softening due to increase in temperature. Additionally, the material parameters in the model can easily be calibrated to match known experimental data and the model has been used to determine the value of the shock pressure at the onset of melting during shock loading.

42 ENGINEERING↗

DOE OSTI2020

A practical way to regularize unfolding of sharply varying spectra with low data statistics

Unfolding is a well-established tool in particle physics. However, a naive application of the standard regularization techniques to unfold the momentum spectrum of protons ejected in the process of negative muon nuclear capture led to a result exhibiting unphysical artifacts. A finite data sample limited the range in which unfolding can be performed, thus introducing a cutoff. A sharply falling “true” distribution led to low data statistics near the cutoff, which exacerbated the regularization bias and produced an unphysical spike in the resulting spectrum. An improved approach has been developed to address these issues and is illustrated using a toy model. The approach uses full Poisson likelihood of data, and produces a continuous, physically plausible, unfolded distribution. Here, the new technique has a broad applicability since sharply falling spectra are common.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2021

Modeling Phase Equilibrium of Common Sugars Glucose, Fructose, and Sucrose in Mixed Solvents

The industrial processing of sugars and sugar-containing mixtures is gaining widespread use as a form of renewable manufacturing from biomass. To aid in process modeling for design and optimization, a commonly available thermodynamic model is needed that describes the phase equilibrium of these compounds. This work compiles and compares models for solid–liquid and vapor–liquid phase equilibrium from the available data for the representative sugars glucose, fructose, and sucrose in the representative solvents water, methanol, and ethanol, including data for multisugar, multisolvent systems. Additionally, the nonrandom two-liquid (NRTL) model was chosen for these systems because of its widespread use in industry and the availability of parameters for many solvents and cosolutes. The association-NRTL (aNRTL) model was investigated as an improvement for modeling sugars, which may experience a high degree of association because of their many hydroxy groups. Both models accurately capture the data and are able to predict the behavior of multisugar systems with only solute–solvent interaction parameters. The aNRTL model shows an improvement over the baseline NRTL model that is most significant for sucrose, the component with the highest association strength, and least significant for fructose, which has the lowest association strength.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

A database of hourly wind speed and modeled generation for US wind plants based on three meteorological models

Abstract In 2022, wind generation accounted for ~10% of total electricity generation in the United States. As wind energy accounts for a greater portion of total energy, understanding geographic and temporal variation in wind generation is key to many planning, operational, and research questions. However, in-situ observations of wind speed are expensive to make and rarely shared publicly. Meteorological models are commonly used to estimate wind speeds, but vary in quality and are often challenging to access and interpret. The Plant-Level US multi-model WIND and generation (PLUSWIND) data repository helps to address these challenges. PLUSWIND provides wind speeds and estimated generation on an hourly basis at almost all wind plants across the contiguous United States from 2018–2021. The repository contains wind speeds and generation based on three different meteorological models: ERA5, MERRA2, and HRRR. Data are publicly accessible in simple csv files. Modeled generation is compared to regional and plant records, which highlights model biases and errors and how they differ by model, across regions, and across time frames.

17 WIND ENERGY↗

DOE OSTI2021

IceCube high-energy starting event sample: Description and flux characterization with 7.5 years of data

The IceCube Neutrino Observatory has established the existence of a high-energy all-sky neutrino flux of astrophysical origin. This discovery was made using events interacting within a fiducial region of the detector surrounded by an active veto and with reconstructed energy above 60 TeV, commonly known as the high-energy starting event sample (HESE). Here, we revisit the analysis of the HESE sample with an additional 4.5 years of data, newer glacial ice models, and improved systematics treatment. This paper describes the sample in detail, reports on the latest astrophysical neutrino flux measurements, and presents a source search for astrophysical neutrinos. We give the compatibility of these observations with specific isotropic flux models proposed in the literature as well as generic power-law-like scenarios. Assuming ν e :ν μ :ν τ =1:1:1, and an equal flux of neutrinos and antineutrinos, we find that the astrophysical neutrino spectrum is compatible with an unbroken power law, with a preferred spectral index of $2.87_{-0.19}^{+0.20}$ for the 68% confidence interval.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Automating Genetic Algorithm Mutations for Molecules Using a Masked Language Model

Inspired by the evolution of biological systems, genetic algorithms have been applied to generate solutions for optimization problems in a variety of scientific and engineering disciplines. For a given problem, a suitable genome representation must be defined along with a mutation operator to generate subsequent generations. Unlike natural systems which display a variety of complex rearrangements (e.g. mobile genetic elements), mutation for genetic algorithms commonly utilizes only random point-wise changes. Furthermore, generalizing beyond point-wise mutations poses a key difficulty as useful genome rearrangements depend on the representation and problem domain. To move beyond the limitations of manually defined point-wise changes, here we propose the use of techniques from masked language models to automatically generate mutations. As a first step, common subsequences within a given population are used to generate a vocabulary. The vocabulary is then used to tokenize each genome. A masked language model is trained on the tokenized data in order to generate possible rearrangements (i.e. mutations). In order to illustrate the proposed strategy, we use string representations of molecules and use a genetic algorithm to optimize for drug-likeness and synthesizability. Finally, our results show that moving beyond random point-wise mutations accelerates genetic algorithm optimization.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

Goated: goal-oriented tensor decompositions in python

SAND2026-20464O Goated performs goal-oriented tensor decompositions in Python, enabling efficient compression of multi-dimensional simulation data. It extends common tensor decomposition methods by incorporating domain-specific knowledge, such as conservation laws in physics, through a penalty term in the optimization process. This approach improves data compression and modeling accuracy across various applications, including physics simulations, by using specialized algorithms and structure-aware subroutines to accelerate solver performance. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525.

SciDAC↗

DOE OSTI2024

Event generators for high-energy physics experiments

We provide an overview of the status of Monte-Carlo event generators for high-energy particle physics. Guided by the experimental needs and requirements, we highlight areas of active development, and opportunities for future improvements. Particular emphasis is given to physics models and algorithms that are employed across a variety of experiments. These common themes in event generator development lead to a more comprehensive understanding of physics at the highest energies and intensities, and allow models to be tested against a wealth of data that have been accumulated over the past decades. A cohesive approach to event generator development will allow these models to be further improved and systematic uncertainties to be reduced, directly contributing to future experimental success. Event generators are part of a much larger ecosystem of computational tools. They typically involve a number of unknown model parameters that must be tuned to experimental data, while maintaining the integrity of the underlying physics models. Making both these data, and the analyses with which they have been obtained accessible to future users is an essential aspect of open science and data preservation. It ensures the consistency of physics models across a variety of experiments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

NASA NTRS1992

Validating a large geophysical data set: Experiences with satellite-derived cloud parameters

We are validating the global cloud parameters derived from the satellite-borne HIRS2 and MSU atmospheric sounding instrument measurements, and are using the analysis of these data as one prototype for studying large geophysical data sets in general. The HIRS2/MSU data set contains a total of 40 physical parameters, filling 25 MB/day; raw HIRS2/MSU data are available for a period exceeding 10 years. Validation involves developing a quantitative sense for the physical meaning of the derived parameters over the range of environmental conditions sampled. This is accomplished by comparing the spatial and temporal distributions of the derived quantities with similar measurements made using other techniques, and with model results. The data handling needed for this work is possible only with the help of a suite of interactive graphical and numerical analysis tools. Level 3 (gridded) data is the common form in which large data sets of this type are distributed for scientific analysis. We find that Level 3 data is inadequate for the data comparisons required for validation. Level 2 data (individual measurements in geophysical units) is needed. A sampling problem arises when individual measurements, which are not uniformly distributed in space or time, are used for the comparisons. Standard 'interpolation' methods involve fitting the measurements for each data set to surfaces, which are then compared. We are experimenting with formal criteria for selecting geographical regions, based upon the spatial frequency and variability of measurements, that allow us to quantify the uncertainty due to sampling. As part of this project, we are also dealing with ways to keep track of constraints placed on the output by assumptions made in the computer code. The need to work with Level 2 data introduces a number of other data handling issues, such as accessing data files across machine types, meeting large data storage requirements, accessing other validated data sets, processing speed and throughput for interactive graphical work, and problems relating to graphical interfaces.

Kahn, Ralph↗

NASA NTRS2002

Measurements of Oxygenated Organic Chemicals In the Pacific Troposphere During TRACE-P: Higher Aldehydes (less than C(sub 1)), Their Sources, and Potential Role In Atmospheric Oxidation

Airborne measurements of a large number of oxygenated organics were carried out in the Pacific troposphere (to 12 km) in the Spring of 2001 (Feb. 24-April 10). Specifically these measurements included acetaldehyde, propanaldehyde, acetone, methylethyl ketone, methanol, ethanol, PAM and organic nitrates. Independent measurements of formaldehyde, peroxides, and tracers were also available. Highly polluted as well as pristine air masses were sampled. Oxygenated organics were abundant in the clean In troposphere and were greatly enhanced in the outflow regions from Asia. Extremely high concentrations of aldehydes could be measured in the troposphere. It is not possible to explain the large abundances of aldehydes in the background troposphere without invoking significant oceanic sources. A strong correlation between the observed mixing ratios of formaldehyde and acetaldehyde is present. We infer that higher aldehydes (such as acetaldehyde and propanaldehyde) may provide a large source of formaldehyde and sequester Cox throughout the troposphere. The atmospheric behavior of acetone, methylethyl ketone, and methanol is generally indicative of their common terrestrial sources with a Image contribution from biomass/biofuel burning. A vast body of data has been collected and it is being analyzed both statistically and with the help of models to better understand the role that oxygenated organics play in the atmosphere and to unravel their sources and sinks. These results will be presented.

Singh, Hanwant B.↗

NASA NTRS2009

Radiation-Induced Amorphization of Crystalline Ice

We study radiation-induced amorphization of crystalline ice, ana lyzing the resu lts of three decades of experiments with a variety of projectiles, irradiation energy, and ice temperature, finding a similar trend of increasing resistance of amorphization with temperature and inconsistencies in results from different laboratories. We discuss the temperature dependence of amorphization in terms of the 'thermal spike' model. We then discuss the common use of the 1.65 micrometer infrared absorption band of water as a measure of degree of crystallinity, an increasingly common procedure to analyze remote sensing data of astronomical icy bodies. The discussion is based on new, high quality near-infrared refl ectance absorption spectra measured between 1.4 and 2.2 micrometers for amorphous and crystalline ices irradiated with 225 keV protons at 80 K. We found that, after irradiation with 10(exp 15) protons per square centimeter, crystalline ice films thinner than the ion range become fully amorphous, and that the infrared absorption spectra show no significant changes upon further irradiation. The complete amorphization suggests that crystalline ice observed in the outer Solar System, including trans-neptunian objects, may results from heat from internal sources or from the impact of icy meteorites or comets.

Fama, M.↗

NASA NTRS2020

Circulating miRNA Signature Predicts Health Risks Associated with Cancer and Spaceflight

Biological risks associated with space radiation and microgravity are major concerns for long-term space travel. Through a Systems Biology approach, our previous NASA work has shown both TGF signaling pathways and miRNAs have a critical impact on defining health risks with and without space irradiation. We hypothesize that circulating microRNA (miRNA) signatures are driving microvascular disease and muscle degeneration associated with accelerating aging and will be enhanced by exposure to the space environment (radiation and microgravity). We are investigating this hypothesis with both in vivo and in vitro models to test novel antagonist therapies to these miRNA signatures as countermeasures to reduce space radiation-induced health risks. A comprehensive Systems Biology approach is utilized to examine the influence by high atomic number by high (H) atomic number (Z) and energy (E) (HZE) irradiation. To simulate low-dose exposure due to galactic cosmic rays (GCR), we used ions, energy, and doses determined by a NASA consensus formula of 7 different ions to represent GCR (referred to as GCR sim model). To similate high-dose radiation exposure due to solar particle events (SPE), we used a solar particle event (SPE) sim model which gave a total dose of 1Gy protons with energy ranges from 50MeV to 150MeV. C57BL/6 wild-type female mice were utilized for the irradiations with our established simulated microgravity model (hindlimb suspension model) and an in vitro 3D microvasculature tissue model under simulated microgravity (clinostat) conditions was also irradiated. To expand on the circulating miRNA signature determined from our preliminary data, we determined a group of conserved miRNAs which are commonly being regulated in the majority of the organs and tissues throughout the host using our established techniques. MiRNA-sequencing was done on serum (at time of sacrifice), liver, heart, and muscle (soleus muscle) tissue for all radiation groups. Additional validation of the key miRNAs was performed by droplet digital PCR (ddPCR). This revealed a key circulating miRNA signature (consisting of multiple miRNAs) impacting cardiovascular and muscular disease risk. Further in vitro experiments with CRISPR/Cas9 system to knockout the key miRNA signatures, novel self-delivering antagomirs, overexpression of the miRNAs test the functional impact of the miRNA signatures on both microvascular disease and muscle degeneration due to space irradiation. The current work has started to allow the possible development of a novel minimally invasive miRNA based radioprotector to be used as a countermeasure for space radiation. Collectively, understanding of how whole body space radiation impacts microvascular and tissue degeneration through circulating miRNAs will greatly enhance health risk prognostication and provide possible new mechanisms for protection against space radiation. This work is supported by the Translational Research Institute through NASA Cooperative Agreement NNX16AO69A (T-0404) awarded to AB.

Beheshti, Afshin↗

DOE OSTI2022

Monotonic Gaussian Process for Physics-Constrained Machine Learning With Materials Science Applications

Physics-constrained machine learning is emerging as an important topic in the field of machine learning for physics. One of the most significant advantages of incorporating physics constraints into machine learning methods is that the resulting model requires significantly less data to train. By incorporating physical rules into the machine learning formulation itself, the predictions are expected to be physically plausible. Gaussian process (GP) is perhaps one of the most common methods in machine learning for small datasets. In this paper, we investigate the possibility of constraining a GP formulation with monotonicity on three different material datasets, where one experimental and two computational datasets are used. The monotonic GP is compared against the regular GP, where a significant reduction in the posterior variance is observed. The monotonic GP is strictly monotonic in the interpolation regime, but in the extrapolation regime, the monotonic effect starts fading away as one goes beyond the training dataset. Imposing monotonicity on the GP comes at a small accuracy cost, compared to the regular GP. The monotonic GP is perhaps most useful in applications where data are scarce and noisy, and monotonicity is supported by strong physical evidence.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Revealing the Crystalline Architecture of Semicrystalline Ion Exchange Membranes for the Design of Conductive and Durable Alkaline Anion Exchange Membranes

Alkaline anion exchange membrane (AAEM) fuel cells offer a cost-effective alternative to proton exchange membrane (PEM) fuel cells by eliminating the need for expensive precious metal catalysts. In both PEMs and AAEMs, semicrystalline polymers are a common choice, as the crystalline domains can act as mechanical reinforcements that limit swelling and promote mechanical durability in the material. However, spatially resolved characterization of crystalline organization in ion exchange membranes beyond ensemble-averaged X-ray scattering is underrepresented, likely in part due to ionization damage limitations in soft materials. Here, in this study, we resolve the nanometer-size crystallites in semicrystalline ion exchange membranes by applying cryogenic four-dimensional scanning transmission electron microscopy (cryo-4D-STEM) along with data-processing algorithms designed to optimize signals at a low dose to minimize radiation damage. We investigate the effects of synthesis components, including molecular weight and thermal treatment, on a model system of AAEMs in comparison to Nafion, the most commonly used and commercially successful PEM today. We find that excess water uptake in polymer membranes, a property directly associated with weak mechanical durability and with possible negative impacts on ion conductivity, can be reduced by over 30% by varying the polymer's crystalline morphology through changes in synthesis parameters such as molecular weight and thermal history. Our results indicate that this improvement is correlated with smaller crystalline domains with a more homogeneous distribution. More broadly, these results demonstrate how the crystalline architecture of polymer membranes can be tuned through their chemistry and thermal treatment in order to improve their conductivity and durability for commercial fuel cell performance.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Deep Learning and Likelihood Approaches for Viral Phylogeography Converge on the Same Answers Whether the Inference Model Is Right or Wrong

Abstract Analysis of phylogenetic trees has become an essential tool in epidemiology. Likelihood-based methods fit models to phylogenies to draw inferences about the phylodynamics and history of viral transmission. However, these methods are often computationally expensive, which limits the complexity and realism of phylodynamic models and makes them ill-suited for informing policy decisions in real-time during rapidly developing outbreaks. Likelihood-free methods using deep learning are pushing the boundaries of inference beyond these constraints. In this paper, we extend, compare, and contrast a recently developed deep learning method for likelihood-free inference from trees. We trained multiple deep neural networks using phylogenies from simulated outbreaks that spread among 5 locations and found they achieve close to the same levels of accuracy as Bayesian inference under the true simulation model. We compared robustness to model misspecification of a trained neural network to that of a Bayesian method. We found that both models had comparable performance, converging on similar biases. We also implemented a method of uncertainty quantification called conformalized quantile regression that we demonstrate has similar patterns of sensitivity to model misspecification as Bayesian highest posterior density (HPD) and greatly overlap with HPDs, but have lower precision (more conservative). Finally, we trained and tested a neural network against phylogeographic data from a recent study of the SARS-Cov-2 pandemic in Europe and obtained similar estimates of region-specific epidemiological parameters and the location of the common ancestor in Europe. Along with being as accurate and robust as likelihood-based methods, our trained neural networks are on average over 3 orders of magnitude faster after training. Our results support the notion that neural networks can be trained with simulated data to accurately mimic the good and bad statistical properties of the likelihood functions of generative phylogenetic models.

Evolutionary Biology↗

NASA NTRS2014

Temperature and Humidity Profiles in the TqJoint Data Group of AIRS Version 6 Product for the Climate Model Evaluation

The Atmospheric Infrared Sounder (AIRS) mission is entering its 13th year of global observations of the atmospheric state, including temperature and humidity profiles, outgoing long-wave radiation, cloud properties, and trace gases. Thus AIRS data have been widely used, among other things, for short-term climate research and observational component for model evaluation. One instance is the fifth phase of the Coupled Model Intercomparison Project (CMIP5) which uses AIRS version 5 data in the climate model evaluation. The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) is the home of processing, archiving, and distribution services for data from the AIRS mission. The GES DISC, in collaboration with the AIRS Project, released data from the version 6 algorithm in early 2013. The new algorithm represents a significant improvement over previous versions in terms of greater stability, yield, and quality of products. The ongoing Earth System Grid for next generation climate model research project, a collaborative effort of GES DISC and NASA JPL, will bring temperature and humidity profiles from AIRS version 6. The AIRS version 6 product adds a new "TqJoint" data group, which contains data for a common set of observations across water vapor and temperature at all atmospheric levels and is suitable for climate process studies. How different may the monthly temperature and humidity profiles in "TqJoint" group be from the "Standard" group where temperature and water vapor are not always valid at the same time? This study aims to answer the question by comprehensively comparing the temperature and humidity profiles from the "TqJoint" group and the "Standard" group. The comparison includes mean differences at different levels globally and over land and ocean. We are also working on examining the sampling differences between the "TqJoint" and "Standard" group using MERRA data.

climate model evaluation↗

NASA NTRS2016

Rare Earth Element Partitioning in Lunar Minerals: An Experimental Study

The partitioning behavior of rare earth elements (REE) between minerals and melts is widely used to interpret the petrogenesis and geologic context of terrestrial and extra-terrestrial samples. REE are important tools for modelling the evolution of the lunar interior. The ubiquitous negative Eu anomaly in lunar basalts is one of the main lines of evidence to support the lunar magma ocean (LMO) hypothesis, by which the plagioclase-rich lunar highlands were formed as a flotation crust during differentiation of a global-scale magma ocean. The separation of plagioclase from the mafic cumulates is thought to be the source of the Eu depletion, as Eu is very compatible in plagioclase. Lunar basalts and volcanic glasses are commonly depleted in light REEs (LREE), and more enriched in heavy REEs (HREE). However, there is very little experimental data available on REE partitioning between lunar minerals and melts. In order to interpret the source of these distinctive REE patterns, and to model lunar petrogenetic processes, REE partition coefficients (D) between lunar minerals and melts are needed at conditions relevant to lunar processes. New data on D(sub REE) for plagioclase, and pyroxenes are now available, but there is limited available data for olivine/melt D(sub REE), particularly at pressures higher than 1 bar, and in Fe-rich and reduced compositions - all conditions relevant to the lunar mantle. Based on terrestrial data, REE are highly incompatible in olivine (i.e. D much less than 1), however olivine is the predominant mineral in the lunar interior, so it is important to understand whether it is capable of storing even small amounts of REE, and how the REEs might be fractionatied, in order to understand the trace element budget of the lunar interior. This abstract presents results from high-pressure and temperature experiments investigating REE partitioning between olivine and melt in a composition relevant to lunar magmatism.

McIntosh, E. C.↗