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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Machine learning inversion from scattering for mechanically driven polymers

A machine learning inversion method is developed for analyzing scattering functions of mechanically driven polymers and extracting the corresponding feature parameters, which include energy parameters and conformation variables. The polymer is modeled as a chain of fixed-length bonds constrained by bending energy, and it is subject to external forces such as stretching and shear. We generate a data set consisting of random combinations of energy parameters, including bending modulus, stretching and shear force, along with Monte Carlo-calculated scattering functions and conformation variables such as end-to-end distance, radius of gyration and off-diagonal component of the gyration tensor. The effects of the energy parameters on the polymer are captured by the scattering function, and principal component analysis ensures the feasibility of the machine learning inversion. Finally, we train a Gaussian process regressor using part of the data set as a training set and validate the trained regressor for inversion using the rest of the data. The regressor successfully extracts the feature parameters.

Gaussian process regressors↗
DOE OSTI2020

Uncovering Non-Fermi-Liquid Behavior in Hund Metals: Conformal Field Theory Analysis of an SU(2)×SU(3) Spin-Orbital Kondo Model

Hund metals have attracted attention in recent years due to their unconventional superconductivity, which supposedly originates from non-Fermi-liquid (NFL) properties of the normal state. When studying Hund metals using dynamical mean-field theory, one arrives at a self-consistent “Hund impurity problem” involving a multiorbital quantum impurity with nonzero Hund coupling interacting with a metallic bath. If its spin and orbital degrees of freedom are screened at different energy scales, T sp < T orb , the intermediate energy window is governed by a novel NFL fixed point, whose nature had not yet been clarified. We resolve this problem by providing an analytical solution of a paradigmatic example of a Hund impurity problem, involving two spin and three orbital degrees of freedom. To this end, we combine a state-of-the-art implementation of the numerical renormalization group, capable of exploiting non-Abelian symmetries, with a generalization of Affleck and Ludwig’s conformal field theory (CFT) approach for multichannel Kondo models. We characterize the NFL fixed point of Hund metals in detail for a Kondo model with an impurity forming an SU ( 2 ) × SU ( 3 ) spin-orbital multiplet, tuned such that the NFL energy window is very wide. The impurity’s spin and orbital susceptibilities then exhibit striking power-law behavior, which we explain using CFT arguments. We find excellent agreement between CFT predictions and numerical renormalization group results. Our main physical conclusion is that the regime of spin-orbital separation, where orbital degrees of freedom have been screened but spin degrees of freedom have not, features anomalously strong local spin fluctuations: the impurity susceptibility increases as χ sp imp ~ ω – γ , with γ > 1 .

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Advances for QCD and the standard model: color-confining light-front holography and the principle of maximum conformality

Here, I review how the application of superconformal quantum mechanics and light-front holography leads to new insights into the physics of color confinement, the spectroscopy and dynamics of hadrons, as well as surprising supersymmetric relations between the masses of mesons, baryons, and tetraquarks. Spontaneous chiral symmetry breaking is automatically fulfilled by supersymmetric Light-Front QCD. The light-front holographic approach (HLFQCD) also predicts the behavior of the QCD running coupling and other observables from the nonperturbative color-confining domain to the perturbative domain. One can determine the QCD running coupling to high precision from the data of just a single experiment over the entire perturbative regime by using the Principle of Maximum Conformality (PMC). The PMC, which generalizes the conventional Gell-Mann-Low method for scale-setting in perturbative QED to non-Abelian QCD, provides a rigorous method for achieving unambiguous scheme-independent, fixed-order Standard Model predictions, consistent with the principles of the renormalization group. I also briefly review a novel feature of hadronic physics predicted by QCD: intrinsic heavy quarks.

Brodsky, Stanley J. [SLAC National Accelerator Lab↗
DOE OSTI2024

Predicting RNA structure and dynamics with deep learning and solution scattering

Advanced deep learning and statistical methods can predict structural models for RNA molecules. However, RNAs are flexible, and it remains difficult to describe their macromolecular conformations in solutions where varying conditions can induce conformational changes. Small-angle x-ray scattering (SAXS) in solution is an efficient technique to validate structural predictions by comparing the experimental SAXS profile with those calculated from predicted structures. There are two main challenges in comparing SAXS profiles to RNA structures: the absence of cations essential for stability and charge neutralization in predicted structures and the inadequacy of a single structure to represent RNA’s conformational plasticity. We introduce a solution conformation predictor for RNA (SCOPER) to address these challenges. This pipeline integrates kinematics-based conformational sampling with the innovative deep learning model, IonNet, designed for predicting Mg 2+ ion binding sites. Validated through benchmarking against 14 experimental data sets, SCOPER significantly improved the quality of SAXS profile fits by including Mg 2+ ions and sampling of conformational plasticity. We observe that an increased content of monovalent and bivalent ions leads to decreased RNA plasticity. Therefore, carefully adjusting the plasticity and ion density is crucial to avoid overfitting experimental SAXS data. SCOPER is an efficient tool for accurately validating the solution state of RNAs given an initial, sufficiently accurate structure and provides the corrected atomistic model, including ions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Optimizing conformance control for gas injection EOR in unconventional reservoirs

Field observations have shown that interference between wells caused by connected fractures commonly exists in unconventional reservoirs. The well interference effect and conformance issues could lead to minimal enhanced oil recovery response (EOR) in actual operations if they are not properly controlled. A series of data analysis, modeling, and experimental activities were performed in this study to investigate the gas EOR strategies in the Bakken Formation. Employing the embedded discrete fracture modeling technology, a multiple-well, multiple-fracture model was developed to capture the well interference and conformance issues in the Bakken. The model made it possible to investigate the complex flow behavior between wells in the EOR (huff ‘n’ puff) processes. Alternative EOR and conformance control strategies using water and surfactant injections were studied to improve oil production performance based on experimental observations. Results showed that water injection could be used for conformance control in the Bakken because of its relatively low mobility compared to gas; however, water could also block oil flow around the conformance control wells because of the high interfacial tension between oil and water. Adding surfactant to the injection water is more effective for improving gas EOR performance since surfactant reduces the oil–water interfacial tension and contact angle effectively. Up to 9.4% more oil could be produced from the huff ‘n’ puff wells in 2 years of EOR operations when rich gas was injected at only 3 MMscf/d with 6000 psi of surfactant injection in the offset wells for conformance control.

42 ENGINEERING↗
DOE OSTI2025

Integrating Ultra-Coarse-Grained Protein Models into Accessible Workflows for Multiscale Molecular Dynamics

To capture protein conformational transitions using molecular dynamics (MD), several simulation resolutions covering different spatial and temporal scales are typically needed. All-atom (AA) simulations provide fine resolution, but are computationally infeasible for large systems over longer durations. Coarse-grained (CG) and ultra-coarse-grained (UCG) models have a lower resolution and computational cost while still being able to conserve essential protein features. Prior work on a Multiscale Machinelearned Modeling Infrastructure (MuMMI) combined both AA and CG simulations to study RAS-RAF protein interactions, leveraging CG models for longer time scales and using AA to investigate unusual conformations in greater detail. However, MuMMI is still resource-intensive, and this study aims to maximize exploration of the protein conformational space while reducing computational cost. In this paper, we build on prior work that integrates UCG models based on heterogeneous elastic network modeling (hENM) into the MuMMI workflow. We demonstrate that UCG models enable accurate sampling of protein conformations, focusing on simulating RAS-RAF protein interactions. Using higher-resolution CG Martini simulation data, we can automatically refine intramolecular interactions in UCG models. We present a scalable Python package that uses fluctuations observed in higher-resolution CG Martini simulations to estimate bond coefficients of the UCG model. We built novel machine learning-based backmapping methods to recover more detailed CG Martini structures from UCG structures, using diffusion models to learn the mapping between scales. Finally, we present UCG-mini-MuMMI, an accessible and less compute-intensive version of MuMMI as a resource for the scientific community. Incorporating UCG models into MD studies is applicable to a broad range of systems and proteins, and our study offers insights into the advantages and limitations of these methods.

Chemical structure↗
DOE OSTI2021

Nonlinear Shear Rheology of Entangled Polymer Rings

Steady-state shear viscosity η($\bar{γ}$) of unconcatenated ring polymer melts as a function of the shear rate $\bar{γ}$ is studied by a combination of experiments, simulations, and theory. Experiments using polystyrenes with Z ≈ 5 and Z ≈ 11 entanglements indicate weaker shear thinning for rings compared to linear polymers exhibiting power law scaling of shear viscosity η ~ $\bar{γ}$ –0.56 ± 0.02 , independent of chain length, for Weissenberg numbers up to about 10 2 . Nonequilibrium molecular dynamics simulations using the bead-spring model reveal a similar behavior with η ~ $\bar{γ}$ -0.57 ± 0.08 for 4 ≤ Z ≤ 57. Viscosity decreases with chain length for high $\bar{γ}$. In our experiments, we see the onset of this regime, and in simulations, which we extended to Wi ~ 10 4 , the nonuniversality is fully developed. In addition to a naive scaling theory yielding for the universal regime η ~ $\bar{γ}$ –0.57 , we developed a novel shear slit model explaining many details of observed conformations and dynamics as well as the chain length-dependent behavior of viscosity at large $\bar{γ}$. The signature feature of the model is the presence of two distinct length scales: the size of tension blobs and much larger thickness of a shear slit in which rings are self-consistently confined in the velocity gradient direction and which is dictated by the size of a chain section with relaxation time 1/$\bar{γ}$. These two length scales control the two normal stress differences. In this model, the chain length-dependent onset of nonuniversal behavior is set by tension blobs becoming as small as about one Kuhn segment. This model explains the approximate applicability of the Cox–Merz rule for ring polymers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Building molecular model series from heterogeneous CryoEM structures using Gaussian mixture models and deep neural networks

Cryogenic electron microscopy (CryoEM) produces structures of macromolecules at near-atomic resolution. However, building molecular models with good stereochemical geometry from those structures can be challenging and time-consuming, especially when many structures are obtained from datasets with conformational heterogeneity. Here we present a model refinement protocol that automatically generates series of molecular models from CryoEM datasets, which describe the dynamics of the macromolecular system and have near-perfect geometry scores. This method makes it easier to interpret the movement of the protein complex from heterogeneity analysis and to compare the structural dynamics observed from CryoEM data with results from other experimental and simulation techniques.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Advanced Computational Modeling of High-Level Waste Vitrification at the Hanford Site

The U.S. Department of Energy (DOE) has selected vitrification for stabilizing legacy tank waste at the Hanford site, where radioactive waste from plutonium production was historically stored in underground tanks. This waste will be separated into low-activity waste (LAW) and high-level waste (HLW) fractions and processed at the Waste Treatment and Immobilization Plant (WTP). At WTP, glass melters are used for the vitrification of radioactive tank waste, transforming it into a stable borosilicate glass form for safe long-term storage. The melter vessel is constructed from highly durable and heat-resistant materials, where the vitrification process occurs. The main regions that are modeled are the melt pool, plenum, cold cap, riser/discharge chamber, and surrounding structure with insulation layers. Forced convection induced by air bubblers at the base of the melter ensure uniform temperature distribution and provide heat to the cold cap layer. The cold cap is a region of reacting batch feed that floats on top of the molten glass and is where the batch-to-glass reactions occur. Joule heating provided by electrodes mounted along the vertical walls of the melter and immersed directly in the glass, generates the necessary heat for the net endothermic conversion processes that occur in the cold cap. The high temperatures, radioactivity, and opaque nature of the glass prevent direct observation inside the melters. Therefore, computational models are essential for providing insight into factors that affect melter throughput. Thermocouples in the plenum provide operators with plenum temperature measurements. Operational adjustments include bubbling rate, voltage supplied to the electrodes, feed adjustments, and glass removal rate. Different computational fluid dynamics (CFD) models have been developed, each serving a specific purpose. There are CFD models of different scale melters, as well as models that capture the two-phase flow interfaces of rising bubbles in the molten glass or models with a simplified molten glass region so that the surrounding structure and plenum can be feasibly incorporated. Pilot-scale melter models have been developed to serve as validation of the methods employed in the simulation of the full-scale WTP melters. Models incorporating resolved bubbling are used to develop momentum source terms to implement into a single phase, multi-region, steady-state flow model that is being validated by measured process parameters such as glass production rate, voltage, input power, plenum temperatures, etc. The resolved bubbling model uses the multiphase volume of fluid approach to model the system with a high-resolution interface capturing scheme to maintain sharp interfaces between the molten glass and the air phase. The suite of CFD models is continually being improved to incorporate more realistic physics and achieve faster turnaround time. For example, an incremental controller is implemented to automatically adjust electrode voltage within the simulation to a molten glass set point temperature of 1150°C. Newer models feature improved meshes to ensure conformal meshes between regions and eliminate unnecessary mesh refinement in areas that are not of interest (such as boundary layers in offgas ports). Instead of explicitly modeling the structural, refractory, and insulation layers of the melter, a thermal resistance approach is used with published correlations used for boundary conditions. The development of robust and efficient CFD models will be instrumental in enabling the WTP to successfully fulfill its mission of safely stabilizing legacy nuclear waste.

12 - MGMT OF RADIOACTIVE AND NON-RADIOACTIVE WASTE↗
DOE OSTI2022

Towards an “$\mathrm{AdS}_1$/$\mathrm{CFT}_0$” correspondence from the $\mathrm{D}$(-1)/$\mathrm{D}$7 system?

We argue that a type IIB Euclidean supergravity solution of the form $\mathbb{R}$ x S 1 x $\mathbb{T}$ 8 with imaginary self-dual F 1 flux through $\mathbb{R}$ x S 1 belongs to the chain of AdS d x S d x $\mathbb{T}$ 10-2d vacua with (imaginary) self-dual Fd flux, where d ≤ 5. Such vacua come from the near-horizon of D(d - 2)/D(8 - d) branes and are supersymmetric for odd values of d. For d = 1 we speculate that the hallmark of conformal symmetry for the matrix model dual is a vanishing free energy. The matrix dual was recently constructed by [1] by adding matrix interactions coming from strings stretching between the D(-1) and D7 branes to the IKKT matrix model. We find that the corresponding supergravity solution indeed has vanishing on-shell action. Specific F 5 fluxes need to be switched on as a consequence of (a T-dual version of) the Hanany-Witten effect.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

A substrate-induced gating mechanism is conserved among Gram-positive IgA1 metalloproteases

The mucosal adaptive immune response is dependent on the production of IgA antibodies and particularly IgA1, yet opportunistic bacteria have evolved mechanisms to specifically block this response by producing IgA1 proteases (IgA1Ps). Our lab was the first to describe the structures of a metal-dependent IgA1P (metallo-IgA1P) produced from Gram-positive Streptococcus pneumoniae both in the absence and presence of its IgA1 substrate through cryo-EM single particle reconstructions. This prior study revealed an active-site gating mechanism reliant on substrate-induced conformational changes to the enzyme that begged the question of whether such a mechanism is conserved among the wider Gram-positive metallo-IgA1P subfamily of virulence factors. Here, we used cryo-EM to characterize the metallo-IgA1P of a more distantly related family member from Gemella haemolysans, an emerging opportunistic pathogen implicated in meningitis, endocarditis, and more recently bacteremia in the elderly. While the substrate-free structures of these two metallo-IgA1Ps exhibit differences in the relative starting positions of the domain responsible for gating substrate, the enzymes have similar domain orientations when bound to IgA1. Together with biochemical studies that indicate these metallo-IgA1Ps have similar binding affinities and activities, these data indicate that metallo-IgA1P binding requires the specific IgA1 substrate to open the enzymes for access to their active site and thus, largely conform to an “induced fit” model.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Structural switching dynamically controls the doubly pseudoknotted Rous sarcoma virus–programmed ribosomal frameshifting element

A hallmark of retrovirus replication is the translation of two different polyproteins from one RNA through programmed –1 frameshifting. This is a mechanism in which the actively translating ribosome is induced to slip in the 5′ direction at a defined codon and then continues translating in the new reading frame. Programmed frameshifting controls the stoichiometry of viral proteins and is therefore under stringent evolutionary selection. Forty years ago, the first frameshifting stimulatory element was discovered in the Rous sarcoma virus. The ~120 nt RNA segment was predicted to contain a pseudoknot, but its 3D structure has remained elusive. Now, we have determined cryoEM and X-ray crystallographic structures of this classic retroviral element, finding that it adopts a butterfly-like double-pseudoknot fold. One “wing” contains a dynamic pyrimidine-rich helix, observed crystallographically in two conformations and in a third conformation via cryoEM. The other wing encompasses the predicted pseudoknot, which interacts with a second unexpected pseudoknot through a toggle residue, A2546. This key purine switches conformations between structural states and tunes the stability of interacting residues in the two wings. We find that its mutation can modulate frameshifting by as much as 50-fold, likely by altering the relative abundance of different structural states in the conformational ensemble of the RNA. Taken together, our structure–function analyses reveal how a dynamic double pseudoknot junction stimulates frameshifting by taking advantage of conformational heterogeneity, supporting a multistate model in which high Shannon entropy enhances frameshifting efficiency.

Science & Technology - Other Topics↗
DOE OSTI2024

Operator product expansion for radial lattice quantization of 3D ϕ 4 theory

At its critical point, the three-dimensional lattice Ising model is described by a conformal field theory (CFT), the 3D Ising CFT. Instead of carrying out simulations on Euclidean lattices, we use the quantum finite elements method to implement radially quantized critical ϕ 4 theory on simplicial lattices approaching R × S 2 . Computing the four-point function of identical scalars, we demonstrate the power of radial quantization by the accurate determination of the scaling dimensions Δ ε and Δ T as well as ratios of the operator product expansion coefficients f σ σ ε and f σ σ T of the first spin-0 and spin-2 primary operators ε and T of the 3D Ising CFT. Published by the American Physical Society 2024

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Quantum Critical Higgs: from AdS 5 to colliders

We examine distinctive signatures of Quantum Critical Higgs models at the LHC and future higher energy colliders. In these models the Higgs boson is part of a conformal sector that is softly broken at a threshold scale, and generically the scaling dimension of the Higgs is larger than in the Standard Model. In particular we examine the gg → H → ZZ, gg → H → γγ, and gg → Z → H Z channels to see how the cross sections deviate from the Standard Model in the high invariant mass region. In order to perform the calculations we use 5D duals of Quantum Critical Higgs models using the AdS/CFT correspondence, with a soft wall to break the conformal symmetry.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

From sequence to protein structure and conformational dynamics with artificial intelligence/machine learning

The 2024 Nobel Prize in Chemistry was awarded in part for de novo protein structure prediction using AlphaFold2, an artificial intelligence/machine learning (AI/ML) model trained on vast amounts of sequence and three-dimensional structure data. AlphaFold2 and related models, including RoseTTAFold and ESMFold, employ specialized neural network architectures driven by attention mechanisms to infer relationships between sequence and structure. At a fundamental level, these AI/ML models operate on the long-standing hypothesis that the structure of a protein is determined by its amino acid sequence. More recently, AlphaFold2 has been adapted for the prediction of multiple protein conformations by subsampling multiple sequence alignments. Herein, we provide an overview of the deterministic relationship between sequence and structure, which was hypothesized over half a century ago with profound implications for the biological sciences ever since. We postulate that protein conformational dynamics are also determined, at least in part, by amino acid sequence and that this relationship may be leveraged for construction of AI/ML models dedicated to predicting protein conformational ensembles. Accordingly, we describe a conceptual model architecture, which may be trained on sequence data in combination with conformationally sensitive structural information, coming primarily from nuclear magnetic resonance (NMR) spectroscopy. Notwithstanding certain limitations in this context, NMR offers abundant structural heterogeneity conducive to conformational ensemble prediction. As NMR and other data continue to accumulate, sequence-informed prediction of protein structural dynamics with AI/ML has the potential to emerge as a transformative capability across the biological sciences.

Artificial intelligence↗
DOE OSTI2022

Calabi-Yau CFTs and random matrices

Using numerical methods for finding Ricci-flat metrics, we explore the spectrum of local operators in two-dimensional conformal field theories defined by sigma models on Calabi-Yau targets at large volume. Focusing on the examples of K3 and the quintic, we show that the spectrum, averaged over a region in complex structure moduli space, possesses the same statistical properties as the Gaussian orthogonal ensemble of random matrix theory.

superstring vacua↗