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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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Thermodynamic Driving Forces for the Self-Assembly of Diblock Polypeptoids
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Towards an advanced benchmark for aerosol radiative forcing: a chemically resolved sectional aerosol scheme in E3SMv3
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Implementing reactivity in molecular dynamics simulations with harmonic force fields
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Forced changes in a Tibetan lake ecosystem over the past millennium
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Database and deep-learning scalability of anharmonic phonon properties by automated brute-force first-principles calculations
Understanding the anharmonic phonon properties of crystal compounds—such as phonon lifetimes and thermal conductivities—is essential for investigating and optimizing their thermal transport behaviors. These properties also impact optical, electronic, and magnetic characteristics through interactions between phonons and other quasiparticles and fields. In this study, we develop an automated first-principles workflow to calculate anharmonic phonon properties and build a comprehensive database encompassing more than 6500 inorganic compounds. Utilizing this dataset, we train a graph neural network model to predict thermal conductivity values and spectra from structural parameters, demonstrating a scaling law in which prediction accuracy improves with increasing training data size. High-throughput screening with the model enables the identification of materials exhibiting extreme thermal conductivities—both high and low. The resulting database offers valuable insights into the anharmonic behavior of phonons, thereby accelerating the design and development of advanced functional materials.
Dark brown carbon from wildfires: a potent snow radiative forcing agent?
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The response of tropical cyclone hazard to natural and forced patterns of warming
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Enhanced detectability of forced signal in monthly precipitation changes
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Thermodynamic driving forces for autoreduction of Cu sites in the zeolite SSZ-13
We use phase diagrams to study the impact of auto-reduction on different Cu sites in the zeolite SSZ-13.
Flexible doorway controlled Na + ion diffusion in NaPSO glassy electrolytes from machine-learning force field simulations
Oxygen doping reduces free volume yet paradoxically enhances amorphous framework flexibility, facilitating Na + ion diffusion. This balance leads to an initial increase, then a decline, observed in conductivity of NaPSO electrolytes.
Analysis of an induced Langmuir wave by ponderomotive forces and its applicability for plasma diagnostics
We present a numerical study on the electron and ion density perturbation in low-temperature plasmas driven by the frequency detuning of two intense laser beams. Our study is performed in the hydrodynamic regime, which becomes applicable when the plasma grating period induced by the beating of the laser beams is greater than the Debye length and collective processes such as plasma oscillations can be excited. Our findings show a resonance in electron density perturbation as the frequency detuning approaches a value consistent with the Bohm–Gross dispersion relation in low- and high-pressure plasmas. We discuss the potential of this resonance as a diagnostic tool for precisely measuring electron temperature and density in low-temperature plasmas through coherent scattering.
Response of salt intrusion in a tidal estuary to regional climatic forcing
Abstract Salinity distribution in a large tidal estuary is subject to estuarine adjustment under the influences of multiple physical drivers such as freshwater pulses and sea level rise, and is crucial to upstream water quality, aquaculture, and ecosystem functions of the estuary. To better understand the estuarine salinity response to climate change, the unstructured-grid Finite Volume Community Ocean Model was implemented to simulate the salt intrusion in the Delaware Bay Estuary. The model was first validated by multiple observational data sets and subsequently applied in an idealized setting to examine the response of salt front to freshwater pulses in high flow conditions, followed by a long-term drought condition supported by a multi-decadal streamflow drought analysis in the estuary. The model results showed that after the freshwater pulses the salt front location moved further upstream with sea level rise. Under the simulated long-term drought condition, the adjustment timescale of salt intrusion varies nonlinearly with sea level rise. With a significant increase in sea level rise, the adjustment timescale starts to decrease. This shift suggests a transition into a different regime where the estuary becomes more stratified, as indicated by an increasing bulk Simpson number with rising sea levels.
Training models using forces computed by stochastic electronic structure methods
Abstract Quantum Monte Carlo (QMC) can play a very important role in generating accurate data needed for constructing potential energy surfaces. We argue that QMC has advantages in terms of a smaller systematic bias and an ability to cover phase space more completely. The stochastic noise can ease the training of the machine learning model. We discuss how stochastic errors affect the generation of effective models by analyzing the errors within a linear least squares procedure, finding that there is an advantage to having many relatively imprecise data points for constructing models. We then analyze the effect of noise on a model of many-body silicon finding that noise in some situations improves the resulting model. We then study the effect of QMC noise on two machine learning models of dense hydrogen used in a recent study of its phase diagram. The noise enables us to estimate the errors in the model. We conclude with a discussion of future research problems.
Multidomain simulations of aluminum nitride with machine-learned force fields
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