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NASA NTRS1990

Dielectric function of InGaAs in the visible

Measurements are reported of the dielectric function of thermodynamically stable In(x)Ga(1-x)As in the composition range 0.3 equal to or less than X = to or less than 0.7. The optically thick samples of InGaAs were made by molecular beam epitaxy (MBE) in the range 0.4 = to or less than X = to or less than 0.7 and by metal-organic chemical vapor deposition (MOCVD) for X = 0.3. The MBE made samples, usually 1 micron thick, were grown on semi-insulating InP and included a strain release structure. The MOCVD sample was grown on GaAs and was 2 microns thick. The dielectric functions were measured by variable angle spectroscopic ellipsometry in the range 1.55 to 4.4 eV. The data was analyzed assuming an optically thick InGaAs material with an oxide layer on top. The thickness of this layer was estimated by comparing the results for the InP lattice matched material, i.e., X = 0.53, with results published in the literature. The top oxide layer mathematically for X = 0.3 and X = 0.53 was removed to get the dielectric function of the bare InGaAs. In addition, the dielectric function of GaAs in vacuum, after a protective arsenic layer was removed. The dielectric functions for X = 0, 0.3, and 0.53 together with the X = 1 result from the literature to evaluate an algorithm for calculating the dielectric function of InGaAs for an arbitrary value of X (0 = to or less than X = to or less than 1) were used. Results of the dielectric function calculated using the algorithm were compared with experimental data.

Alterovitz, S. A.↗

DOE OSTI2021

Metagenomic strategies identify diverse integron–integrase and antibiotic resistance genes in the Antarctic environment

The objective of this study is to identify and analyze integrons and antibiotic resistance genes (ARGs) in samples collected from diverse sites in terrestrial Antarctica. Integrons were studied using two independent methods. One involved the construction and analysis of intI gene amplicon libraries. In addition, we sequenced 17 metagenomes of microbial mats and soil by high-throughput sequencing and analyzed these data using the IntegronFinder program. As expected, the metagenomic analysis allowed for the identification of novel predicted intI integrases and gene cassettes (GCs), which mostly encode unknown functions. However, some intI genes are similar to sequences previously identified by amplicon library analysis in soil samples collected from non-Antarctic sites. ARGs were analyzed in the metagenomes using ABRIcate with CARD database and verified if these genes could be classified as GCs by IntegronFinder. We identified 53 ARGs in 15 metagenomes, but only four were classified as GCs, one in MTG12 metagenome (Continental Antarctica), encoding an aminoglycoside-modifying enzyme (AAC(6´)acetyltransferase) and the other three in CS1 metagenome (Maritime Antarctica). One of these genes encodes a class D β-lactamase (blaOXA-205) and the other two are located in the same contig. One is part of a gene encoding the first 76 amino acids of aminoglycoside adenyltransferase (aadA6), and the other is a qacG2 gene.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRSDate not supplied

Modeling Ring-Opening Polymerization with Machine-Learned Committors

Many important industrial and biological processes depend on ring-opening polymerization (ROP), including large-scale production polymers, such as nylon 6, and a variety of biodegradable polymers and high-performance resins. Large-scale simulations (>200K atoms) of ROP were performed using a deep neural network-based method that captured the effect of the local chemical environment of the reactive sites using training data from quantum chemical methods. The utility of this method was demonstrated by modeling the cationic ROP of bis-benzoxazine, a promising resin for use in advanced high-temperature application composites (>200°C). Density functional theory calculations were used to map out the various ring opening and polymerization reaction paths that occur during the thermal processing of bis-benzoxazine. The probabilities of these reactions, as a function of local atomic configurations, were used to construct committor functions using a neural network approach. The learned committor functions were then used to determine when a particular reaction occurred within the REACTER framework for reactive molecular dynamics simulations. The experimentally observed exotherm that occurs during bis-benzoxazine ROP was captured in an effective manner in the simulations by imposing a local heating of the system that corresponds to the enthalpy of reaction for each reaction. The dependence of the final polymer morphology on the ROP reaction temperature was analyzed at the molecular scale. The results presented in this work indicate that machine-learned committor functions are a promising approach for incorporating high-fidelity reactivity criteria that capture the effect of local chemical environments into large-scale polymerization simulations.

machine learning, polymer simulations, molecular d↗

DOE OSTI2021

Ab initio electronic density in solids by many-body plane-wave auxiliary-field quantum Monte Carlo calculations

We present accurate many-body results of the electronic densities in several solid materials, including Si, NaCl, and Cu. These results are obtained using the ab initio auxiliary-field quantum Monte Carlo (AFQMC) method working in a plane-wave basis with norm-conserving, multiple-projector pseudopotentials. AFQMC has been shown to be an excellent many-body total energy method. Computation of observables and correlation functions other than the ground-state energy requires back-propagation, whose adaption and implementation in the plane-wave basis AFQMC framework are discussed in the present paper. This development allows us to compute correlation functions, electronic densities, and interatomic forces, paving the way for geometry optimizations and calculations of thermodynamic properties in solids. Finite supercell size effects are considerably more subtle in the many-body framework than in independent-electron calculations. We analyze the convergence of the electronic density, and obtain best estimates for the thermodynamic limit. The densities from several typical density functionals are benchmarked against our near-exact results. The electronic densities we obtained can also be used to help construct improved density functionals.

36 MATERIALS SCIENCE↗

DOE OSTI2018

Spectral and Structural Characteristics for Cluster Systems of Charged Brownian Particles

We report on the results of analytic and numerical investigations of the dynamics for bounded ensembles of charged Brownian particles in the potential field of an electrostatic trap. Simulation has been performed for cluster systems consisting of (approximately up to one thousand) particles with the Coulomb interaction in a wide range of their parameters. The spectral structures and structural characteristics of the systems being simulated have been compared. The dependence of the form of the pair correlation function and the size of cluster systems on the temperature and the number of particles has been analyzed. The relation between the spectral density of displacements of the center of mass and of individual particles and the structural characteristics and nonideality parameter of the ensembles being modeled has been investigated.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2025

Heterogeneous Mixtures of Dictionary Functions to Approximate Subspace Invariance in Koopman Operators: Why Deep Koopman Operators Work

Abstract Koopman operators model nonlinear dynamics as a linear dynamic system acting on a nonlinear function as the state. This nonstandard state is often called a Koopman observable and is usually approximated numerically by a superposition of functions drawn from a dictionary . In a widely used algorithm, extended dynamic mode decomposition (EDMD), the dictionary functions are drawn from a fixed class of functions. Deep learning combined with EDMD has been used to learn novel dictionary functions in an algorithm called deep dynamic mode decomposition (deepDMD). The learned representation both (1) accurately models and (2) scales well with the dimension of the original nonlinear system. In this paper, we analyze the learned dictionaries from deepDMD and explore the theoretical basis for their strong performance. We explore State-Inclusive Logistic Lifting (SILL) dictionary functions to approximate Koopman observables. Error analysis of these dictionary functions show they satisfy a property of subspace approximation, which we define as uniform finite approximate closure. Typically, a Koopman dictionary’s nonlinear functions are homogeneous. In this paper, we discover that structured mixing of heterogeneous dictionary functions drawn from different classes of nonlinear functions achieve the same accuracy and dimensional scaling as the deep-learning-based deepDMD algorithm Yeung et al. ( In: 2019 American Control Conference (ACC), 2019). We specifically show this by building a heterogeneous dictionary comprised of SILL functions and conjunctive radial basis functions (RBFs). This mixed dictionary achieves similar accuracy and dimensional scaling to deepDMD with an order of magnitude reduction in parameters, while maintaining geometric interpretability. These results strengthen the viability of dictionary-based Koopman models to solving high-dimensional nonlinear learning problems.

Johnson, Charles A.↗

DOE OSTI2023

Assisted migration in a warmer and drier climate: less climate buffering capacity, less facilitation and more fires at temperate latitudes?

Assisted tree migration has been proposed as a conceptual solution to mitigate lags in biotic responses to anthropogenic climate change. The rationale behind this concept is that tree species currently growing under warmer and drier climates will be more resistant and resilient to the new climatic conditions than tree species naturally growing in currently wetter and colder climates. However, we hypothesize that, by being more stress‐tolerant to warmer and drier conditions, translocated species should exhibit different functional attributes, which could induce important ecological and societal costs and overcome the desired benefits of maintaining wood production and other ecosystem services. We used principal component analysis (PCA) to analyze variation in seven traits of 106 tree and tall shrub species from contrasting latitudinal distributions in western North America and Europe to predict the potential functional changes of forest ecosystems due to the translocation of tree species from low to high latitudes. We show that species from both continents differed primarily by their position on the leaf economy spectrum (LES) and their size traits. Even though, in Europe, differences in LES were significantly correlated to species southern latitudinal positions, in both continents differences in size traits were significantly correlated to latitude. These results suggest that assisted migration by translocating more conservative species of shorter stature in currently cooler climates should decrease the buffering capacity of forest canopies, decrease facilitation for understory species, and increase wildfire risks, whose effects have the potential to accelerate climate warming through negative atmospheric feedback processes. As an alternative solution to assisted migration that may accelerate rather than mitigate climate change, we recommend that foresters gradually diversify the vertical structure and layering of the existing forest canopy to maintain a sustainable water cycle and energy balance between the soil, the tree and the atmosphere without increasing the wildfire risk.

Environmental Sciences & Ecology↗

DOE OSTI2018

Fluorescent resonance energy transfer -based biosensor for detecting conformational changes of Pin1

Highlights: • Pin1 contains two domains: WW domain and catalytic PPIase domain. • Interaction of both domains is required for Pin1 functions. • Molecular movement of Pin1 after it binds each substrate is still unclear. • FRET biosensor for detecting conformational changes of Pin1 was created. • This FRET biosensor is useful for Pin1-targeting drug screening. Pin1, a peptidyl prolyl cis/trans isomerase (PPIase), regulates the activity and stability of various phosphorylated proteins. Pin1 consists of a PPIase domain and WW domain, both of which are required for the function of Pin1. However, how the behavior of these domains changes upon binding to phosphorylated proteins has not been analyzed. We created a Fluorescent Resonance Energy Transfer (FRET)-based biosensor “CPinY”, which is composed of Pin1 flanked by CFP and YFP, and analyzed the interaction between Pin1 and c-Myc. Our results indicated that the dual phosphorylation of c-Myc at Thr58 and Ser62 is essential for tight interaction with Pin1. Additionally, this interaction caused a significant conformational change in Pin1. Our CPinY biosensor also detected a novel type of inhibitor of Pin1 function. We believe that his biosensor will be a novel drug screening technology targeting Pin1.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2019

Probing universalities in d > 2 CFTs: from black holes to shockwaves

Gravitational shockwaves are insensitive to higher-curvature corrections in the action. Recent work found that the OPE coefficients of lowest-twist multi-stress-tensor operators, computed holographically in a planar black hole background, are insensitive as well. In this paper, we analyze the relation between these two limits. We explicitly evaluate the two-point function on a shockwave background to all orders in a large central charge expansion. In the geodesic limit, we find that the ANEC exponentiates in the multi-stress-tensor sector. To compare with the black hole limit, we obtain a recursion relation for the lowest-twist products of two stress tensors in a spherical black hole background, letting us efficiently compute their OPE coefficients and prove their insensitivity to higher curvature terms. After resumming the lowest-twist stress-tensors and analytically continuing their contributions to the Regge limit, we find a perfect agreement with the shockwave computation. We also discuss the role of double-trace operators, global degenerate states, and multi-stress-tensor conformal blocks. These holographic results suggest the existence of a larger universal structure in higher-dimensional CFTs.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

Multiscale Modeling Meets Machine Learning: What Can We Learn?

Machine learning is increasingly recognized as a promising technology in the biological, biomedical, and behavioral sciences. There can be no argument that this technique is incredibly successful in image recognition with immediate applications in diagnostics including electrophysiology, radiology, or pathology, where we have access to massive amounts of annotated data. However, machine learning often performs poorly in prognosis, especially when dealing with sparse data. This is a field where classical physics-based simulation seems to remain irreplaceable. In this review, we identify areas in the biomedical sciences where machine learning and multiscale modeling can mutually benefit from one another: Machine learning can integrate physics-based knowledge in the form of governing equations, boundary conditions, or constraints to manage ill-posted problems and robustly handle sparse and noisy data; multiscale modeling can integrate machine learn- ing to create surrogate models, identify system dynamics and parameters, analyze sensitivities, and quantify uncertainty to bridge the scales and understand the emergence of function. With a view towards applications in the life sciences, we discuss the state of the art of combining machine learning and multiscale modeling, identify applications and opportunities, raise open questions, and address potential challenges and limitations. We anticipate that it will stimulate discussion within the community of computational mechanics and reach out to other disciplines including mathematics, statistics, computer science, artificial intelligence, biomedicine, systems biology, and precision medicine to join forces towards creating robust and efficient models for biological systems.

machine learning, multiscale modeling, physics-bas↗

DOE OSTI2023

The impact of non-ideal phase change properties on phase change thermal energy storage device performance

Phase change materials have been known to improve the performance of energy storage devices by shifting or reducing thermal/electrical loads. While an ideal phase change material is one that undergoes a sharp, reversible phase transition, real phase change materials do not exhibit this behavior and often have one or more non-idealities – glide, hysteresis, supercooling – associated with them. Experimental and modeling techniques to characterize these non-ideal properties are reasonably well understood, however, their impact on the performance of a thermal energy storage system is not fully understood. Herein, we analyze the performance of a heat exchanger with a phase change material as a function of the different non-idealities, for a heating and a cooling application. We focus on the impact of these non-ideal behaviors on different modes of operation of the thermal energy storage device, which will serve as a useful guide to researchers on the relative importance of the non-ideal properties, along with the necessary accuracy required during an experimental characterization.

25 ENERGY STORAGE↗

DOE OSTI2018

Influence of crystallographic orientation and applied magnetic field on domain structure in duplex stainless steel studied using magnetic force microscopy

Highlights: • Ferrite phase in a duplex stainless steel consists of stripe type domains. • Domain contrast decreases with deviation of plane normal from <100>. • Domain width increases with deviation of plane normal from <100>. • Domain alignment with applied field observed along <100> directions. • Grain boundary stray field is a function of misorientation between grains. - Abstract: The paper investigates the local micro-magnetization behavior in duplex stainless steel (DSS) using magnetic force microscopy (MFM). DSS has equal amount of austenite phase in ferrite matrix. The MFM imaging clearly differentiates ferrite phase (ferromagnetic) from the austenite phase (paramagnetic) due to the maze type magnetic domains present in the ferrite phase. The magneto crystalline anisotropic effect on the domain structure is discussed with the support of electron backscatter diffraction (EBSD) measurements. The MFM phase contrast and domain width are correlated with the orientation of surface normal with respect to the easy axis of magnetization (<100>). The influence of external magnetic field (±127 kA/m) application on the domain structure is studied as a function of the grain orientation and the leakage field at the grain boundaries is analyzed based on the mis-orientation between the grains.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Role of Electron Correlation beyond the Active Space in Achieving Quantitative Predictions of Spin-Phonon Relaxation

Single-molecule magnets (SMMs) are promising candidates for molecular-scale data storage and processing due to their strong magnetic anisotropy and long spin relaxation times. However, as temperature rises, interactions between electronic states and lattice vibrations accelerate spin relaxation, significantly limiting their practical applications. Recently, ab initio simulations have made it possible to advance our understanding of phonon-induced magnetic relaxation, but significant deviations from experiments have often been observed. The description of molecules’ electronic structure has been mostly based on complete active space self-consistent field (CASSCF) calculations, and the impact of electron correlation beyond the active space remains largely unexplored. In this study, we provide the first systematic investigation of spin-phonon relaxation in SMMs with post-CASSCF multiconfigurational methods, specifically CAS followed by second-order perturbation theory and multiconfiguration pair-density functional theory. Taking Co(II)- and Dy(III)-based SMMs as case studies, we analyze how electron correlation influences spin-phonon relaxation rates across a range of temperatures, comparing theoretical predictions with experimental observations. Our findings demonstrate that post-CASSCF treatments make it possible to achieve quantitative predictions for Co(II)-based SMMs. For Dy(III)-based systems, however, accurate predictions require consideration of additional effects, underscoring the urgent necessity of further advancing the study of the effects of electronic correlation in these complex systems.

Energy↗

DOE OSTI2018

Physical Origins of the Transient Absorption Spectra and Dynamics in Thin-Film Semiconductors: The Case of BiVO 4

Transient absorption (TA) spectroscopy is uniquely suited for understanding kinetic processes initiated by light over vast ranges of time. In combination with white light probes, the recorded differential absorption spectra can contain spectroscopic signatures characteristic of specific charge carrier population densities. However, disentangling the often-complex and convoluted spectra is made challenging without robust analysis methods relating the underlying physical mechanisms to the spectral components. In this work, we address the origin of the transient spectra of a model system of emerging solar energy harvesting materials using a monoclinic BiVO 4 thin film. Using ground-state optical properties of the semiconductor, we find the main derivative-like spectral response to be related to shifting and broadening of oscillators, rather than specific carrier-related transitions. However, by using the Drude optical model of free carriers, we also identify the transient response related to free-hole density. Importantly, sample heating from the optical pumping, which begins at ~10 ps and plateaus by ~200 ps, dominates the overall spectral response at longer times. On the basis of a physical model of the spectral response, a kinetic model is developed that describes the pump power dependence of the free-hole density, as well as the temporal evolution of the spectral changes associated with shifting and broadening of oscillators. First-principles density functional theory calculations are used to rationalize experimental measurements. Here, this comprehensive approach to analyzing and modeling the TA spectra offers a generalizable basis for understanding the complex pump-probe data, reveals thermal heating artifacts that are frequently erroneously assigned to long-lived photocarriers, and offers a path to eliminating ambiguity in analysis of photocarrier dynamics in solid-state systems.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Evaluating the Effects of Precipitation and Evapotranspiration on Soil Moisture Variability Within CMIP5 Using SMAP and ERA5 Data

=The effects of precipitation (Pr) and evapotranspiration (ET) on surface soil moisture (SSM) play an essential role in the land-atmosphere system. Here we evaluate multimodel differences of these effects within the Coupled Model Intercomparison Project Phase 5 (CMIP5) compared to Soil Moisture Active Passive (SMAP) products and ECMWF Reanalysis v5 (ERA5) as references in a frequency domain. The variability of SSM, Pr, and ET within three frequency bands (1/7 ~ 1/30 days -1 , 1/30 ~ 1/90 days -1 , and 1/90 ~ 1/365 days -1 ) after normalization is quantified using Fourier transform. We analyze the impact of ET and Pr on SSM variability based on a transfer function assuming that these variables form a linear time-invariant (LTI) system. For the total effects of ET and Pr on SSM variability, the CMIP5 estimations are smaller than the reference data in the two higher frequency bands and are larger than the reference data in the lowest frequency band. Besides, the effects on SSM by Pr and ET are found to be different across the three frequency bands. In each frequency band, the variability of the factor that dominates SSM (i.e., Pr or ET) from CMIP5 is smaller than that from the references. This study identifies the spatiotemporal distribution of differences between CMIP5 models and references (SMAP and ERA5) in simulating ET and Pr effects on SSM within three frequency bands. This study provides insightful information on how soil moisture variability is affected by varying precipitation and evapotranspiration at different time scales within Earth System Models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Chiral algebra, localization, modularity, surface defects, and all that

We study the 2D vertex operator algebra (VOA) construction in 4D N = 2 superconformal field theories on S 3 × S 1 , focusing on both old puzzles and new observations. The VOA lives on a two-torus T 2 ⊂ S 3 × S 1 , it is 1 2 Z -graded, and this torus is equipped with the natural choice of spin structure (1,0) for the Z + 1 2 -graded operators, corresponding to the NS sector vacuum character. By analyzing the possible refinements of the Schur index that preserves the VOA, we find that it admits discrete deformations, which allows access to the remaining spin structures (1,1), (0,1), and (0,0), of which the latter two involve the inclusion of a particular surface defect. For Lagrangian theories, we perform the detailed analysis: we describe the natural supersymmetric background, perform localization, and derive the gauged symplectic boson action on a torus in any spin structure. In the absence of flavor fugacities, the 2D and 4D path integrals precisely match, including the Casimir factors. We further analyze the 2D theory: we identify its integration cycle and the two-point functions and interpret flavor holonomies as screening charges in the VOA. Next, we make some observations about modularity; the T-transformation acts on our four partition functions and lifts to a large diffeomorphism on S 3 × S 1 . More interestingly, we generalize the four partition functions on the torus to an infinite family labeled by both the spin structure and the integration cycle inside the complexified maximal torus of the gauge group. Members of this family transform into one another under the full modular group, and we confirm the recent observation that the S-transform of the Schur index in Lagrangian theories exhibits logarithmic behavior. Finally, we comment on how locally our background reproduces the Ω-background.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Sensitivity Analysis of Effectiveness of FLEX Strategies Using a CDF-Based Importance Measure Under Accident Conditions

One of the lessons learned from the Fukushima Daiichi nuclear power plant (NPP) accident is strengthening the station blackout (SBO) mitigation capabilities by enhancing defense in depth for all existing and new NPPs. One of the possible remedies is diverse and flexible coping strategies (FLEX). The objective of this study is to address the benefits of FLEX in various accident scenarios in terms of both risk and cost. FLEX was originally devised against SBO accidents. In this research, we investigate the fundamental plant responses against accidents considering two fundamentally different cases: accidents that lead to high pressure on the primary side and accidents that lead to low pressure on the primary side. Several uncertainties are associated with the characteristics of the FLEX portable equipment. Specifically, the time for FLEX deployment may depend on several factors such as type of accident, point of injection, availability of safety systems, battery backup timings, and human actions. This study utilizes a dynamic risk assessment framework to analyze accident scenarios and suggests a novel importance measure, which is a cumulative distribution function– based importance metric that characterizes the influence of input distribution on complete output distribution. The importance of the existing and newly developed FLEX strategy based on risk significance is illustrated with examples. The suggested measure provides clear insight into how FLEX affects risk of the whole system and additional risk margins thanks to new safety systems.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2019

Diamond Magnetic Microscopy of Malarial Hemozoin Nanocrystals

Magnetic microscopy of malarial hemozoin nanocrystals is performed by optically detected magnetic resonance imaging of near-surface diamond nitrogen-vacancy centers. Hemozoin crystals are extracted from Plasmodium falciparum–infected human blood cells and studied alongside synthetic hemozoin crystals. The stray magnetic fields produced by individual crystals are imaged at room temperature as a function of the applied field up to 350 mT. More than 100 nanocrystals are analyzed, revealing the distribution of their magnetic properties. Most crystals (96%) exhibit a linear dependence of the stray-field magnitude on the applied field, confirming hemozoin’s paramagnetic nature. A volume magnetic susceptibility of 3.4 × 10 –4 is inferred with use of a magnetostatic model informed by correlated scanning-electron-microscopy measurements of crystal dimensions. A small fraction of nanoparticles (4/82 for Plasmodium falciparum–produced nanoparticles and 1/41 for synthetic nanoparticles) exhibit a saturation behavior consistent with superparamagnetism. Translation of this platform to the study of living Plasmodium-infected cells may shed new light on hemozoin formation dynamics and their interaction with antimalarial drugs.

59 BASIC BIOLOGICAL SCIENCES↗