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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Persistence length regulates emergent dynamics in active roller ensembles

Active colloidal fluids, biological and synthetic, often demonstrate complex self-organization and the emergence of collective behavior. Spontaneous formation of multiple vortices has been recently observed in a variety of active matter systems, however, the generation and tunability of the active vortices not controlled by geometrical confinement remain challenging. Here, we exploit the persistence length of individual particles in ensembles of active rollers to tune the formation of vortices and to orchestrate their characteristic sizes. We use two systems and employ two different approaches exploiting shape anisotropy or polarization memory of individual units for control of the persistence length. We characterize the dynamics of emergent multi-vortex states and reveal a direct link between the behavior of the persistence length and properties of the emergent vortices. We further demonstrate common features between the two systems including anti-ferromagnetic ordering of the neighboring vortices and active turbulent behavior with a characteristic energy cascade in the particles velocity field energy spectra. Finally, our findings provide insights into the onset of spatiotemporal coherence in active roller systems and suggest a control knob for manipulation of dynamic self-assembly in active colloidal ensembles.

42 ENGINEERING↗
DOE OSTI2023

A quantum trajectory picture of single photon absorption and energy transport in photosystem II

Here, we use quantum trajectory theory to study the dynamics of the first step in photosynthesis for a single photon interacting with photosystem II (PSII). By considering individual trajectories we are able to look beyond the ensemble average dynamics to compute the PSII system evolution conditioned upon individual photon counting measurements. Measurements of the transmitted photon beam strongly affects the system state, since detection of an outgoing photon confirms that the PSII must be in the electronic ground state, while a null measurement implies it is in an excited electronic state. We show that under ideal conditions, observing the null result transforms a state with a low excited state population to a state with nearly all population contained in the excited states. We study the PSII dynamics conditioned on such photon counting for both a pure excitonic model of PSII and a more realistic model with exciton-phonon coupling to a dissipative phononic environment. In the absence of such coupling, we show that the measured fluorescence rates show oscillations constituting a photon-counting witness of excitonic coherence. Excitonic coupling to the phonon environment has a strong effect on the observed rates of fluorescence, damping the oscillations. Addition of non-radiative decay and incoherent transitions to radical pair states in the reaction center to the phononic model allows extraction of a quantum efficiency of 92.5% from the long-time evolution, consistent with bulk experimental measurements.

Coherent states↗
DOE OSTI2024

Stochastic symplectic reduced-order modeling for model-form uncertainty quantification in molecular dynamics simulations in various statistical ensembles

Here, this work focuses on the representation of model-form uncertainties in molecular dynamics simulations in various statistical ensembles. In prior contributions, the modeling of such uncertainties was formalized and applied to quantify the impact of, and the error generated by, pair-potential selection in the microcanonical ensemble (NVE). In this work, we extend this formulation and present a linear-subspace reduced-order model for the canonical (NVT) and isobaric (NPT) ensembles. The symplectic reduced-order basis is randomized on the tangent space of the Stiefel manifold to provide topological relationships and capture model-form uncertainty. Using the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS), we assess the relevance of these stochastic reduced-order atomistic models on canonical problems involving a Lennard-Jones fluid and an argon crystal melt.

42 ENGINEERING↗
DOE OSTI2025

Entropy is an important design principle in the photosystem II supercomplex

Photosystem II (PSII) can achieve near-unity quantum efficiency of light harvesting in ideal conditions and can dissipate excess light energy as heat to prevent the formation of reactive oxygen species (ROS) under light stress. Understanding how this pigment–protein complex accomplishes these opposing goals is a topic of great interest that has so far been explored primarily through the lens of the system energetics. Despite PSII’s known flat energy landscape, a thorough consideration of the entropic effects on energy transfer in PSII is lacking. In this work, we aim to discern the free energetic design principles underlying the PSII energy transfer network. To accomplish this goal, we employ a structure-based rate matrix and compute the free energy terms in time following a specific initial excitation to discern how entropy and enthalpy drive ensemble system dynamics. We find that the interplay between the entropy and enthalpy components differ among each protein subunit, which allows each subunit to fulfill a unique role in the energy transfer network. This individuality ensures that PSII can accomplish efficient energy trapping in the reaction center (RC), effective nonphotochemical quenching (NPQ) in the periphery, and robust energy trapping in the other-monomer RC if the same-monomer RC is closed. We also show that entropy, in particular, is a dynamically tunable feature of the PSII free energy landscape accomplished through regulation of LHCII binding. These findings help rationalize natural photosynthesis and provide design principles for more efficient solar energy harvesting technologies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

An Ensemble Score Filter for Tracking High-Dimensional Nonlinear Dynamical Systems

We propose an ensemble score filter (EnSF) for solving high-dimensional nonlinear filtering problems with superior accuracy. A major drawback of existing filtering methods, e.g., particle filters or ensemble Kalman filters, is the low accuracy in handling high-dimensional and highly nonlinear problems. EnSF attacks this challenge by exploiting the score-based diffusion model, defined in a pseudo-temporal domain, to characterizing the evolution of the filtering density. EnSF stores the information of the recursively updated filtering density function in the score function, in stead of storing the information in a set of finite Monte Carlo samples (used in particle filters and ensemble Kalman filters). Unlike existing diffusion models that train neural networks to approximate the score function, we develop a training-free score estimation that uses mini-batch-based Monte Carlo estimator to directly approximate the score function at any pseudo-spatial-temporal location, which provides sufficient accuracy in solving high-dimensional nonlinear problems as well as saves tremendous amount of time spent on training neural networks. Another essential aspect of EnSF is its analytical update step, gradually incorporating data information into the score function, which is crucial in mitigating the degeneracy issue faced when dealing with very high-dimensional nonlinear filtering problems. High-dimensional Lorenz systems are used to demonstrate the performance of our method. EnSF provides surprisingly impressive performance in reliably tracking extremely high-dimensional Lorenz systems (up to 1,000,000 dimension) with highly nonlinear observation processes, which is a well-known challenging problem for existing filtering methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Extended experimental inferential structure determination method in determining the structural ensembles of disordered protein states

Proteins with intrinsic or unfolded state disorder comprise a new frontier in structural biology, requiring the characterization of diverse and dynamic structural ensembles. Here we introduce a comprehensive Bayesian framework, the Extended Experimental Inferential Structure Determination (X-EISD) method, which calculates the maximum log-likelihood of a disordered protein ensemble. X-EISD accounts for the uncertainties of a range of experimental data and back-calculation models from structures, including NMR chemical shifts, J-couplings, Nuclear Overhauser Effects (NOEs), paramagnetic relaxation enhancements (PREs), residual dipolar couplings (RDCs), hydrodynamic radii (R h ), single molecule fluorescence Förster resonance energy transfer (smFRET) and small angle X-ray scattering (SAXS). We apply X-EISD to the joint optimization against experimental data for the unfolded drkN SH3 domain and find that combining a local data type, such as chemical shifts or J-couplings, paired with long-ranged restraints such as NOEs, PREs or smFRET, yields structural ensembles in good agreement with all other data types if combined with representative IDP conformers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1976

Dispersion of ensembles of non-interacting particles

The dynamics of an ensemble of noninteracting particles dispersing from a common origin and moving in a common force field with an initial distribution of momenta is analyzed using an approach where the particles are considered as a continuum described by a phase-space distribution function. General solutions are obtained for both the distribution function and the associated spatial density function. The linear case of small departures from circular orbits in an axisymmetric gravitational field is treated along with the specific case of particle dispersion from an object in a circular orbit in the same type of field. Numerical results are presented for the latter case, and consideration is given to the inverse problem of determining the initial time and velocity distribution from knowledge of the ensemble structure at a later time. Explicit results are provided for the case of an ellipsoidal distribution of initial momenta, and a numerical procedure is indicated for treating more general cases.

Heard, W. B.↗
DOE OSTI2024

A Novel Emergent Constraint Approach for Refining Regional Climate Model Projections of Peak Flow Timing

Abstract Global climate models (GCMs) are unable to produce detailed runoff conditions at the basin scale. Assumptions are commonly made that dynamical downscaling can resolve this issue. However, given the large magnitude of the biases in downscaled GCMs, it is unclear whether such projections are credible. Here, we use an ensemble of dynamically downscaled GCMs to evaluate this question in the Sierra‐Cascade mountain range of the western US. Future projections across this region are characterized by earlier seasonal shifts in peak flow, but with substantial inter‐model uncertainty (−25 ± 34.75 days, 95% confidence interval (CI)). We apply the emergent constraint (EC) method for the first time to dynamically downscaled projections, leading to a 39% (−28.25 ± 20.75 days, 95% CI) uncertainty reduction in future peak flow timing. While the constrained results can differ from bias corrected projections, the EC is based on GCM biases in historical peak flow timing and has a strong physical underpinning.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Connecting energetics to dynamics in particle growth by oriented attachment using real-time observations

The interplay between crystal and solvent structure, interparticle forces and ensemble particle response dynamics governs the process of crystallization by oriented attachment (OA), yet a quantitative understanding is lacking. Using as a model system ZnO, we combine in situ TEM observations of single particle and ensemble assembly dynamics with simulations of interparticle forces and responses to relate experimentally derived interparticle potentials to the underlying interactions. We show that OA is driven by forces and torques due to a combination of electrostatic ion-solvent correlations and dipolar interactions that act at separations well beyond 5 nm. Importantly, coalignment is achieved before particles reach separations at which strong attractions drive the final jump to contact. The observed barrier to attachment is negligible, while dissipative factors in the quasi-2D confinement of the TEM fluid cell lead to abnormal diffusivities with timescales for rotation much less than for translation, thus enabling OA to dominate.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Uncertainty of 21st Century western U.S. snowfall loss derived from regional climate model large ensemble

Abstract The western United States is dependent on winter snowfall over its major mountain ranges, which gradually melts each year, serving as a natural reservoir for water resources. In a future warmer climate, much of this snowfall could be replaced by rain, making it more challenging to capture and store water. In this study, we utilize an ensemble of dynamically downscaled simulations forced by 14 global climate models (GCMs). These GCMs project wildly different futures, in terms of both temperature and precipitation change, producing significant uncertainty in snowfall projections. Here we exploit the robust statistics of the downscaled ensemble, and diagose the sensitivity of end-of-century snowfall loss across the region to both warming and regional wetting/drying in the driving GCM. The windward slopes of the Sierra Nevada and Cascades are particularly sensitive to warming (losing ~ 15% annual snowfall per degree warming), with little influence of precipitation. By contrast, snowfall loss in the inter-mountain west is less sensitive to warming (~ 5% K −1 ), but is significantly offset/exacerbated by precipitation changes (~ 0.5% snow per 1% precipitation). Combining such sensitivities with the warming and regional precipitation signals in the full CMIP6 ensemble, we can fully quantify likely snowfall loss and its uncertainty at any location, for any emissions scenario. We find that the western U.S. as a whole will lose 34 ± 8% of its total volumetric snowfall by end-of-century under the high-emissions SSP3-7.0 scenario, but 25 ± 6% and 17 ± 6% under the lower-emissions SSP2-4.5 and SSP1-2.6 scenarios.

Norris, Jesse (ORCID:0000000289883326)↗
DOE OSTI2021

Random matrix theory of the isospectral twirling

We present a systematic construction of probes into the dynamics of isospectral ensembles of Hamiltonians by the notion of Isospectral twirling, expanding the scopes and methods of ref. [1]. The relevant ensembles of Hamiltonians are those defined by salient spectral probability distributions. The Gaussian Unitary Ensembles (GUE) describes a class of quantum chaotic Hamiltonians, while spectra corresponding to the Poisson and Gaussian Diagonal Ensemble (GDE) describe non chaotic, integrable dynamics. We compute the Isospectral twirling of several classes of important quantities in the analysis of quantum many-body systems: Frame potentials, Loschmidt Echos, OTOCs, Entanglement, Tripartite mutual information, coherence, distance to equilibrium states, work in quantum batteries and extension to CP-maps. Moreover, we perform averages in these ensembles by random matrix theory and show how these quantities clearly separate chaotic quantum dynamics from non chaotic ones.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Global Carbon Budget 1800-2018 Loss of Additional Sink Capacity and Present Transient Difference

A dataset of two global carbon fluxes: the loss of additional sink capacity (LASC) and present versus transient difference (PTD). The loss of additional sink capacity is the loss of indirect anthropogenic sink capacity in land ecosystems caused by the loss of ecosystems that can increase carbon in response to changing environments, primarily forests. The present transient difference refers to the direct anthropogenic flux in land ecosystems (from land cover and land use change) and the difference in that flux when calculated assuming "present day" (actually more like 1980) ecosystem carbon stocks or when calculated assuming transient carbon stocks (i.e. caused by environmental change). In this dataset, the LASC and PTD fluxes were calculated using the TRENDY ensemble of Dynamic Global Vegetation Models (DGVMs; Sitch et al., 2024) following the methods described therein and in Obermeier et al. (2021). For further details see Obermeier et al. (2021), Friedlingstein et al. (2019), Sitch et al. (2024), and Walker et al. (2025). The dataset includes ensemble means and standard deviations for annual fluxes, fluxes cumulated annually over the whole time period, and cumulated since 1959. These data were originally collected to compare different approaches (DGVMs versus bookkeeping models) for estimating land use and land cover change (LULCC) emissions (Obermeier et al. 2021). More recently, and similarly, these data were used to harmonize the estimates of the direct and indirect anthropogenic fluxes in the global carbon cycle and to recalculate the global carbon budget (Walker et al., 2025).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

An overview of the Western United States Dynamically Downscaled Dataset (WUS-D3)

Abstract. Predicting future climate change over a region of complex terrain, such as the western United States (US), remains challenging due to the low resolution of global climate models (GCMs). Yet the climate extremes of recent years in this region, such as floods, wildfires, and drought, are likely to intensify further as climate warms, underscoring the need for high-quality and high-resolution predictions. Here, we present an ensemble of dynamically downscaled simulations over the western US from 1980–2100 at 9 km grid spacing, driven by 16 latest-generation GCMs. This dataset is titled the Western US Dynamically Downscaled Dataset (WUS-D3). We describe the challenges of producing WUS-D3, including GCM selection and technical issues, and we evaluate the simulations' realism by comparing historical results to temperature and precipitation observations. The future downscaled climate change signals are shaped in physically credible ways by the regional model's more realistic coastlines and topography. (1) The mean warming signals are heavily influenced by more realistic snowpack. (2) Mean precipitation changes are often consistent with wetting on the windward side of mountain complexes, as warmer, moister air masses are uplifted orographically during precipitation events. (3) There are large fractional precipitation increases on the lee side of mountain complexes, leading to potentially significant changes in water resources and ecology in these arid landscapes. (4) Increases in precipitation extremes are generally larger than in the GCMs, driven by locally intensified atmospheric updrafts tied to sharper, more realistic gradients in topography. (5) Changes in temperature extremes are different from what is expected by a shift in mean temperature and are shaped by local atmospheric dynamics and land surface feedbacks. Because of its high resolution, comprehensiveness, and representation of relevant physical processes, this dataset presents a unique opportunity to evaluate societally relevant future changes in western US climate.

Rahimi, Stefan (ORCID:0000000331884462)↗
DOE OSTI2022

Predicting the structural basis of targeted protein degradation by integrating molecular dynamics simulations with structural mass spectrometry

Targeted protein degradation (TPD) is a promising approach in drug discovery for degrading proteins implicated in diseases. A key step in this process is the formation of a ternary complex where a heterobifunctional molecule induces proximity of an E3 ligase to a protein of interest (POI), thus facilitating ubiquitin transfer to the POI. In this work, we characterize 3 steps in the TPD process. (1) We simulate the ternary complex formation of SMARCA2 bromodomain and VHL E3 ligase by combining hydrogen-deuterium exchange mass spectrometry with weighted ensemble molecular dynamics (MD). (2) We characterize the conformational heterogeneity of the ternary complex using Hamiltonian replica exchange simulations and small-angle X-ray scattering. (3) We assess the ubiquitination of the POI in the context of the full Cullin-RING Ligase, confirming experimental ubiquitinomics results. Differences in degradation efficiency can be explained by the proximity of lysine residues on the POI relative to ubiquitin.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Dynamically downscaled seasonal heat wave projections in the CONUS

Heat waves are a well-documented hazard that are projected to increase in intensity, duration, and frequency with climate change. Regions of the US experience widely varying temperatures; for example, 35 °C is extremely hot for spring in the Northeast but not for summer in the Southeast. It is important to evaluate projections within a regional context and at a high enough resolution to understand the risks to populations. We identify heat waves across the Conterminous US (CONUS) under SSP5–8.5 from 2020 to 2059 with an ensemble of dynamically downscaled Coupled Model Intercomparison Project Phase 6 (CMIP6) model outputs. We demonstrate that there are regional differences caused by seasonal and local drivers of persistent hot temperatures. Summer heat waves are increasing in intensity and duration faster than winter heat waves because of the atmospheric conditions that promote these events. Our analysis emphasizes the value of fine-resolution modeling for projecting future climate risks.

Rubin, Hannah [University of Tennessee, Knoxville ↗
DOE OSTI2021

Deep learning of accurate force field of ferroelectric HfO 2

The discovery of ferroelectricity in HfO 2 -based thin films opens up new opportunities for using this silicon-compatible ferroelectric to realize low-power logic circuits and high-density nonvolatile memories. The functional performances of ferroelectrics are intimately related to their dynamic responses to external stimuli such as electric fields at finite temperatures. Molecular dynamics is an ideal technique for investigating dynamical processes on large length and time scales, though its applications to new materials are often hindered by the limited availability and accuracy of classical force fields. Here we present a deep neural network–based interatomic force field of HfO 2 learned from ab initio data using a concurrent learning procedure. The model potential is able to predict structural properties such as elastic constants, equation of states, phonon dispersion relationships, and phase transition barriers of various hafnia polymorphs with accuracy comparable with density functional theory calculations. Here, the validity of this model potential is further confirmed by the reproduction of experimental sequences of temperature-driven ferroelectric-paraelectric phase transitions of HfO 2 with isobaric-isothermal ensemble molecular dynamics simulations. We suggest a general approach to extend the model potential of HfO 2 to related material systems including dopants and defects.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗