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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 937 records · Page 52
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Simulating Crystallization in a Colloidal System Using State Predictive Information Bottleneck Based Enhanced Sampling

Here, we investigate crystal nucleation in supersaturated colloid suspensions using enhanced molecular dynamics simulations augmented with machine learning techniques. The simulations reveal that crystallization in the model colloidal system studied here, with particles interacting through a repulsive screened Coulomb Yukawa potential, proceeds from vapor to dense liquid droplet to crystalline phases across multiple high barriers. Employing a one-dimensional reaction coordinate derived from the State Predictive Information Bottleneck framework, our simulations capture back-and-forth phase transitions across multiple barriers effectively in biased metadynamics simulations. We obtain relative free energy differences between different phases and also quantify the roles of different molecular level features in driving the phase changes.

Chemistry↗
DOE OSTI2025

Theoretical Investigations of Anharmonic Effects and Phonon Transport in the Cubic Phase of Crystalline Perovskite CsPbCl 3

The role of anharmonic effects on lattice dynamics and thermal transport has been investigated in the cubic phase of the CsPbCl 3 perovskite. Limitations of the harmonic approximation, which lead to phonon instabilities in the Brillouin zone, were addressed based on self-consistent phonon theory in combination with first-principles calculations and by incorporation of frequency renormalization effects from bubble and loop diagrams. This theoretical approach demonstrates significant improvements compared to results obtained using a self-consistent phonon dynamic matrix. Both the cubic-to-tetragonal phase transition temperature as well as the predicted lattice thermal conductivity values show good agreement with selected sets of available experimental data. In conclusion, the accuracy of the predicted thermal conductivity data is also tested against systems significantly larger than those allowed by quantum calculations, by using molecular dynamic simulations with machine-learning force fields.

Lattices↗
DOE OSTI2025

Electric Field’s Dueling Effects through Dehydration and Ion Separation in Driving NaCl Nucleation at Charged Nanoconfined Interfaces

Investigating nucleation in charged nanoconfined environments under electric fields is crucial for many scientific and engineering applications. Here we study the nucleation of NaCl from aqueous solution near charged surfaces using machine-learning-augmented enhanced sampling molecular dynamics simulations. Our simulations successfully drive phase transitions between the liquid and solid phases of NaCl. The solid phase is stabilized under electric fields, particularly at an intermediate surface charge density. We examine which physical characteristics drive the nucleation of NaCl from aqueous solutions and find that the removal of solvent water from Cl– at the solid precursor surface plays a more critical role than the accumulation of ions. Our simulations reveal the competing effects of electric fields on nucleation processes: they facilitate the removal of water, promoting nucleation, but also promote the separation of ion pairs, thereby hindering nucleation. Here, this work provides a framework for studying nucleation processes in nanoconfined environments under electric fields and provides physical insights for the design of electrochemistry materials.

Electric fields↗
DOE OSTI2026

Solid-state NMR and theoretical studies illuminate lanthanum borohydride C–H borylation catalysts confined within a zeolite

Zeolite-supported single-site lanthanum borohydride catalyzes C–H borylation of hydrocarbons, while the related silica-supported complex is inactive under comparable conditions. The identical composition of support-La(BH 4 ) 2 (THF) 2 sites in the two materials implies that the support bestows underlying structural features onto La that are required for bond activation catalysis, yet the nature of these effects, which could include confinement effects and/or electronic modulation of the site itself, remains to be identified. We used solid-state nuclear magnetic resonance (SSNMR) spectroscopy and molecular dynamics simulations with machine-learning potentials (ML-MD) to analyze the electronic and steric effects imparted by the faujasite support on the precatalyst structure to correlate with catalytic activity. ML-MD simulations show that THF dissociates from La under the influence of confinement, leading to coordinatively unsaturated sites in the zeolite pores. Then, the La complex grafts on Brønsted acid sites (La BAS ) or isolated silanols (La SiO ) or remains physisorbed in the zeolite pores. Catalytic studies comparing compounds supported on faujasite zeolites containing or lacking BAS and/or silanols show that only the former complexes lead to active sites, ruling out confinement as the sole requirement for catalysis. The DFT calculations and ML-MD simulations also reveal that the surface-lanthanum coordination number is two (bidentate) for La BAS , with the metal center forming long, flexible bonds to two oxygen atoms bridging Si and Al, but only one oxygen atom (monodentate) for La SiO . The structure–activity relationship identifies confined, BAS-grafted species as active sites and provides important guidance for the design of enhanced atom-efficient catalysts.

Cui, Jinlei [Ames Laboratory, and Iowa State Univ.↗
DOE OSTI2023

Probing DNA structural heterogeneity by identifying conformational subensembles of a bicovalently bound cyanine dye

DNA is a re-configurable, biological information-storage unit, and much remains to be learned about its heterogeneous structural dynamics. For example, while it is known that molecular dyes templated onto DNA exhibit increased photostability, the mechanism by which the structural dynamics of DNA affect the dye photophysics remains unknown. Here, we use femtosecond, two-dimensional electronic spectroscopy measurements of a cyanine dye, Cy5, to probe local conformations in samples of single-stranded DNA (ssDNA–Cy5), double-stranded DNA (dsDNA–Cy5), and Holliday junction DNA (HJ–DNA–Cy5). A line shape analysis of the 2D spectra reveals a strong excitation–emission correlation present in only the dsDNA–Cy5 complex, which is a signature of inhomogeneous broadening. Molecular dynamics simulations support the conclusion that this inhomogeneous broadening arises from a nearly degenerate conformer found only in the dsDNA–Cy5 complex. These insights will support future studies on DNA’s structural heterogeneity.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2023

Melting curves of ice polymorphs in the vicinity of the liquid–liquid critical point

The possible existence of a liquid–liquid critical point in deeply supercooled water has been a subject of debate due to the challenges associated with providing definitive experimental evidence. The pioneering work by Mishima and Stanley [Nature 392, 164–168 (1998)] sought to shed light on this problem by studying the melting curves of different ice polymorphs and their metastable continuation in the vicinity of the expected liquid–liquid transition and its associated critical point. Based on the continuous or discontinuous changes in the slope of the melting curves, Mishima [Phys. Rev. Lett. 85, 334 (2000)] suggested that the liquid–liquid critical point lies between the melting curves of ice III and ice V. Herein we explore this conjecture using molecular dynamics simulations with a machine learning model based on ab initio quantum-mechanical calculations. We study the melting curves of ices III, IV, V, VI, and XIII and find that all of them are supercritical and do not intersect the liquid–liquid transition locus. We also find a pronounced, yet continuous, change in the slope of the melting lines upon crossing of the liquid locus of maximum compressibility. Finally, we analyze the literature in light of our findings and conclude that the scenario in which the melting curves are supercritical is favored by the most recent computational and experimental evidence. Although the preponderance of evidence is consistent with the existence of a second critical point in water, the behavior of ice polymorph melting lines does not provide strong evidence in support of this viewpoint, according to our calculations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

From Optimization to Sampling Through Gradient Flows

Optimization and sampling algorithms play a central role in science and engineering as they enable finding optimal predictions, policies, and recommendations, as well as expected and equilibrium states of complex systems. The notion of “optimality” is formalized by the choice of an objective function, while the notion of an “expected” state is specified by a probabilistic model for the distribution of states. Optimizing rugged objective functions and sampling multimodal distributions is computationally challenging, especially in high-dimensional problems. Here, for this reason, many optimization and sampling methods have been developed by researchers working in disparate fields such as Bayesian statistics, molecular dynamics, genetics, quantum chemistry, machine learning, weather forecasting, econometrics, and medical imaging.

Trillos, N. García↗
DOE OSTI2026

Altered morphology and diffusivity of water confined in MXenes: Machine learning–accelerated computations combined with experiments

Nanoconfined water exhibits unique properties compared to bulk water due to limited quantities, frustrated hydrogen bonding, and surface interactions, which are fundamental for energy storage and transport applications. We integrate machine learning–accelerated ab initio molecular dynamics with x-ray diffraction (XRD) and inelastic neutron scattering (INS) to systematically analyze the thermodynamic and dynamic behavior of water confined between functionalized (-F, -O, and -OH) two-dimensional (2D) Ti 3 C 2 T x MXene layers. As water intercalates between layers, the interlayer spacing exhibits layer-dependent staging characteristics. The water polarization can be flipped by the count and morphology of intercalated molecules interacting with MXene surface groups, resulting in varying electrostatic potential profiles. On the basis of interfacial electrostatic potential, hydrogen bond lifetime, and molecular orientation, we establish a linear combination of exponential model describing water diffusivity. These computational insights align well with experimental x-ray and neutron measurements, suggesting strategies for tuning water morphology and transport by tailoring MXene surface chemistry and water content for electrochemical energy storage and nanofluidic applications.

Tang, Jiawei [Southeast Univ., Nanjing (China)] (O↗
DOE OSTI2018

Sparse Hardware Embedding of Spiking Neuron Systems for Community Detection

We study the applicability of spiking neural networks and neuromorphic hardware for solving general optimization problems without the use of adaptive training or learning algorithms. We leverage the dynamics of Hopfield networks and spin-glass systems to construct a fully connected spiking neural system to generate synchronous spike responses indicative of the underlying community structure in an undirected, unweighted graph. Mapping this fully connected system to current generation neuromorphic hardware is done by embedding sparse tree graphs to generate only the leading-order spiking dynamics. Here, we demonstrate that for a chosen set of benchmark graphs, the spike responses generated on a current generation neuromorphic processor can improve the stability of graph partitions and non-overlapping communities can be identified even with the loss of higher-order spiking behavior if the graphs are sufficiently dense. For sparse graphs, the loss of higher-order spiking behavior improves the stability of certain graph partitions but does not retrieve the known community memberships.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

pnnl/deps_arXiv2020

This software implements data-driven control and dynamic systems models using deep learning methods. The particular application is building temperature control.

Tuor, Aaron↗
DOE OSTI2022

gdess: A framework for evaluating simulated atmospheric CO 2 in Earth System Models

Atmospheric carbon dioxide (CO 2 ) plays a key role in the global carbon cycle and global warming. Climate-carbon feedbacks are often studied and estimated using Earth System Models (ESMs), which couple together multiple model components—including the atmosphere, ocean, terrestrial biosphere, and cryosphere—to jointly simulate mass and energy exchanges within and between these components. Despite tremendous advances, model intercomparisons and benchmarking are aspects of ESMs that warrant further improvement (Fer et al., 2021; Smith et al., 2014). Such benchmarking is critical because comparing the value of state variables in these simulations against observed values provides evidence for appropriately refining model components; moreover, researchers can learn much about Earth system dynamics in the process (Randall et al., 2019). We introduce `gdess` (a.k.a., Greenhouse gas Diagnostics for Earth System Simulations), which parses observational datasets and ESM simulation output, combines them to be in a consistent structure, computes statistical metrics, and generates diagnostic visualizations. In its current incarnation, `gdess` facilitates evaluating a model's ability to reproduce observed temporal and spatial variations of atmospheric CO 2 . The diagnostics implemented modularly in `gdess` support more rapid assessment and improvement of model-simulated global CO 2 sources and sinks associated with land and ocean ecosystem processes. We intend for this set of automated diagnostics to form an extensible, open source framework for future comparisons of simulated and observed concentrations of various greenhouse gases across Earth system models.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Cooperative Research and Development Agreement With Georgetown University Report: National Institutes of Health, National Center for Advancing Translational Sciences Clinical and Translational Science Award

Oak Ridge National Laboratory (ORNL) is participating with Georgetown University (GU) as a subrecipient in response to the National Institutes of Health (NIH), National Center for Advancing Translational Sciences (NCATS) Clinical and Translational Science Award (CTSA) (U54 Clinical Trial Optional) funding opportunity announcement. This Cooperative Research and Development Agreement is put in to place to facilitate the development and implementation of clinical interventions that demonstrably improve human health is currently a complex, recursive, and inefficient process that leads to delays of years or decades before discoveries in biomedical research result in health benefits for patients and communities. NCATS conducts and supports research in the science of translation, to discover the mechanistic and operational principles of the intervention development and dissemination process, thereby providing the scientific foundation for improvements in translational efficiency that will accelerate the realization of interventions that improve human health. Under NCATS’ leadership, the CTSA Program supports a national network of medical research institutions called hubs. GU is the lead institution in one of the NIH hubs that was created as a result of a previous NIH CTSA. The missions of the GU have historically included the advancement of health through research in the clinical and biomedical sciences, the education of future leaders in medical and nursing practice and academia, and the provision of compassionate and scientifically competent patient care and service to the Washington, DC community and the nation. GU is the lead institution for the Georgetown-Howard Universities Center for Clinical and Translational Science (GHUCCTS), a multi-institutional partnership of medical research institutions forged from a desire to promote clinical research and translational science. Through multiple collaborations among these institutions, GHUCCTS is transforming clinical research and translational science in order to bring new scientific advances to health care. Oak Ridge National Laboratory is the Department of Energy's (DOE) largest science and energy laboratory. Managed since April 2000 by a partnership of the University of Tennessee and Battelle, ORNL was established in 1943 as a part of the secret Manhattan Project to pioneer a method for producing and separating plutonium. During the 1950s and 1960s, ORNL became an international center for the study of nuclear energy and related research in the physical and life sciences. With the creation of DOE in the 1970s, ORNL's mission broadened to include a variety of energy technologies and strategies. Today the laboratory supports the nation with a peacetime science and technology mission that is just as important as, but very different from, its role during the Manhattan Project. ORNL is home to the world's premier center for high performance supercomputing to enable scientific discovery. ORNL has extensive expertise in various areas of computer science that are uniquely situated to support GU. Additionally, ORNL’s leading computational user facilities present a unique opportunity to leverage the largest scale machines for open science in support of the stated mission of the NCATS CTSA. ORNL's partnership with GU will offer unparalleled opportunity in data analytics, deep-learning, artificial intelligence, and urban dynamics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Cooperative Research and Development Agreement between National Energy Technology Laboratory and University of Kentucky (Abstract)

The National Energy Technology Laboratory (NETL) and University of Kentucky (Participant) will collaborate in the development of “Deep Learning Potential-based Molecular Dynamics Simulation Investigation of Chemical Conversion-Absorption Coupling of CO 2 at Air-Reactive Deep Eutectic Solvent Interface.” This project is supported by DOE-Office of Science Graduate Student Research (SCGSR) program and managed by Oak Ridge Institute for Science and Education (ORISE). Deep eutectic solvents (DESs) are promising alternative sorbents for direct air capture (DAC) CO 2 due to their low vapor pressure, low corrosion, non-toxicity, relatively low cost, and biodegradable nature. Understanding the CO 2 reaction-transport mechanisms at the interface can help develop cost-effective DAC by DESs with high capacity and stability. The collaboration will investigate the absorption and chemical conversion of CO 2 at the air-DES interface to facilitate the development of novel DAC technology using reactive DESs. The anticipated results will make DAC more efficient, allow more sustainable use of our Nation’s fossil energy resources, and advance NETL’s ongoing CO 2 capture efforts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Unlocking the Sun's Secrets: Laboratory Insights into the Sun's Outer Atmosphere

The Sun’s corona, its outermost and hottest atmospheric layer, emits brightly in the extreme ultraviolet (EUV) and X-rays. This emission comes from atoms that have been highly ionized by the high temperature, forming a plasma of atomic ions and free electrons. By studying this light, scientists learn about the complex and dynamic drivers of solar activity. Many past, current, and future solar observatories focus on measuring EUV spectra from the Fe IX ion, as this ion provides powerful diagnostics for solar physics. Using Fe IX spectra, solar physicists can measure the plasma temperature, density, elemental abundances, and flow velocities in the corona. However, atomic data uncertainties concerning spectral line identification and plasma density diagnostics are a major problem for the accurate interpretation of these spectra.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

Uncertainty in Synthetic Tropical Cyclone Hazard and Risk Estimates: Insights from RAFT, CHAZ, MIT, STORM, and CLIMADA

We synthesize five complementary tropical cyclone (TC) hazard frameworks—RAFT (physics-based machine learning), CHAZ and MIT (statistical–dynamical), STORM (fully statistical), and CLIMADA (observation-driven resampling)—to characterize uncertainty in wind-related TC metrics relevant to energy applications. All datasets and the IBTrACS observational record are harmonized to a common 6-hourly, 2.5° grid. We compare basin-wide and coastal properties using consistent definitions for TC frequency, mean and maximum intensity, 24-hour intensification, and 6-hour translation speed, and quantify agreement with Pearson r, RMSE, and Kling–Gupta efficiency (KGE) alongside resampling-based confidence intervals. CLIMADA is included for basin context but excluded from coastal skill scoring because it resamples historical IBTrACS; if supplied with projected future tracks from an external hazard model, CLIMADA can be used to simulate future TC scenarios. Results show robust, cross-model signals: (i) a corridor of activity from the tropical Atlantic through the Caribbean into the Bahamas and western subtropical Atlantic; (ii) a meridional dipole in 24-hour intensification (low-latitude strengthening, subtropical weakening); and (iii) a transition from slower tropical motion to faster midlatitude translation. Coastal winds (mean and maximum) consistently cluster from the eastern Gulf into the Bahamas–western Atlantic transition. The largest structural spread occurs in the amplitude and footprint of lifetime maximum intensity and, secondarily, in translation speed; intensification exhibits similar central behavior across frameworks with variability in extremes. Translation speed shows the most uniform coastal agreement. These findings provide a decision envelope for wind-focused risk screening and clarify where uncertainty should be carried forward; wind-only results represent a lower bound on total hazard, motivating integration of surge and rainfall modules and a companion, asset-level damage analysis.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Short-Term Rainfall Prediction Based on Radar Echo Using an Improved Self-Attention PredRNN Deep Learning Model

Accurate short-term precipitation forecast is extremely important for urban flood warning and natural disaster prevention. In this paper, we present an innovative deep learning model named ISA-PredRNN (improved self-attention PredRNN) for precipitation nowcasting based on radar echoes on the basis of the advanced PredRNN-V2. We introduce the self-attention mechanism and the long-term memory state into the model and design a new set of gating mechanisms. To better capture different intensities of precipitation, the loss function with weights was designed. We further train the model using a combination of reverse scheduled sampling and scheduled sampling to learn the long-term dynamics from the radar echo sequences. Experimental results show that the new model (ISA-PredRNN) can effectively extract the spatiotemporal features of radar echo maps and obtain radar echo prediction results with a small gap from the ground truths. From the comparison with the other six models, the new ISA-PredRNN model has the most accurate prediction results with a critical success index (CSI) of 0.7001, 0.5812 and 0.3052 under the radar echo thresholds of 10 dBZ, 20 dBZ and 30 dBZ, respectively.

Wu, Dali (ORCID:0000000231177074)↗
DOE OSTI2020

Deep anomaly detection for industrial systems: a case study

We explore the use of deep neural networks for anomaly detection of industrial systems where the data are multivariate time series measurements. We formulate the problem as a self-supervised learning where data under normal operation are used to train a deep neural network autoregressive model, i.e., use a window of time series data to predict future data values. The aim of such a model is to learn to represent the system dynamic behavior under normal conditions, while expect higher model vs. measurement discrepancies under faulty conditions. In real world applications, many control settings are discrete in nature. In this paper, vector embedding and joint losses are employed to deal with such situations. Both LSTM and CNN based deep neural network backbones are studied on the Secure Water Treatment (SWaT) testbed datasets. Also, Support Vector Data Description (SVDD) method is adapted to such anomaly detection settings with deep neural networks. Evaluation methods and results are discussed based on the SWaT dataset along with potential pitfalls.

anomaly detection, deep neural network↗
NASA NTRS1982

Influence of maneuverability on helicopter combat effectiveness

A computational procedure employing a stochastic learning method in conjunction with dynamic simulation of helicopter flight and weapon system operation was used to derive helicopter maneuvering strategies. The derived strategies maximize either survival or kill probability and are in the form of a feedback control based upon threat visual or warning system cues. Maneuverability parameters implicit in the strategy development include maximum longitudinal acceleration and deceleration, maximum sustained and transient load factor turn rate at forward speed, and maximum pedal turn rate and lateral acceleration at hover. Results are presented in terms of probability of skill for all combat initial conditions for two threat categories.

Falco, M.↗