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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 1,009 records · Page 56
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Scalability analysis of heavy-duty gas turbines using data-driven machine learning

With the increasing integration of variable renewable energy sources into power systems, the role of flexible power generation technologies like gas turbines (GT) in rapid grid balancing remains crucial. This sustained importance underscores the need for scaled and precise modeling of GT to ensure effective integration within evolving energy frameworks. While physics-driven GT models integrate thermodynamics, fluid dynamics, and combustion principles, they often rely on approximate mathematical representations to accommodate scaling that may not capture the actual complex dynamics for GTs and inertial effects associated to GTs with different ratings. In this study, a data-driven model is proposed using machine learning (ML) techniques to conduct GT scalability analysis and performance evaluation with high accuracy. The ML model, trained on data from various operating conditions and performance parameters, aims to uncover intricate relationships and patterns, resembling GT characteristics at different scales (ratings). The model is developed to capture complex system interaction and to adapt to changing operational scenarios at different capacities, providing valuable insights of power system dynamics. In this study, the real-time digital simulator platform was employed to generate training data for the ML model and assess its dynamic characteristics. The ultimate objective was to develop a detailed modeling framework based on governing equations and data-driven ML capable of predicting key performance indicators, in thermal systems such as GTs, including power output, speed, fuel consumption, and exhaust temperature under diverse operating conditions at different scales. The developed ML framework demonstrated high accuracy, with mean relative errors for GT power prediction, reference speed, exhaust temperature, and compressor pressure ratio (CPR) parameters consistently below 0.1% across typical load fluctuation scenarios. Maximum deviations were limited to approximately 0.5 K for exhaust temperature and 0.009 for CPR, underscoring the model’s ability to replicating dynamic GT behavior with high precision. The adaptability of the ML model enables its application across diverse operational conditions and its extension to other thermal systems. By leveraging advanced ML techniques, this study presents a robust and scalable modeling framework that enhances GT simulation precision, facilitating improved integration into evolving power systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Critical mineral source potential from oil & gas produced waters in the United States

The volume of produced water, a by-product of oil & gas operations and other energy processes, has been growing across the United States (U.S.) along with the need to manage or recycle this wastewater. Produced water contains many naturally occurring elements of varying concentrations, including critical minerals which are essential to the clean energy transition. However, the current understanding of critical mineral concentrations in produced water and the associated volumes across the U.S. is limited. This study has assessed available databases and literature to gain insight into the presence and concentration of five high priority critical minerals, namely cobalt, lithium, magnesium, manganese, and nickel. The U.S. Geological Survey's National Produced Waters Geochemical Database was the main data source used for determining average critical mineral concentrations in produced water from the major oil and gas reservoirs in the U.S. The volumes of produced water for these major reservoirs were coupled with these concentrations to provide insights into where critical minerals are likely to have high abundance and therefore more recovery options. The analysis indicated the highest recovery potential for lithium and magnesium from produced water in the Permian basin and the Marcellus shale region. However, these assessments should be considered conservative due to the limited availability of reliable concentration data. Finally, it is expected more critical mineral recovery options could emerge with comprehensive characterization data from more recent and representative sources of produced water.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Contrasting Trivalent Lanthanide and Actinide Complexation by Polyoxometalates via Solution-State NMR

Deciphering the solution chemistry and speciation of actinides is inherently difficult due to radioactivity, rarity, and cost constraints, especially for transplutonium elements. In this context, the development of new chelating platforms for actinides and associated spectroscopic techniques is particularly important. In this study, we investigate a relatively overlooked class of chelators for actinide binding, namely, polyoxometalates (POMs). We provide the first NMR measurements on americium–POM and curium–POM complexes, using one-dimensional (1D) 31 P NMR, variable-temperature NMR, and spin-lattice relaxation time (T 1 ) experiments. The proposed POM–NMR approach allows for the study of trivalent f-elements even when only microgram amounts are available and in phosphate-containing solutions where f-elements are typically insoluble. The solution-state speciation of trivalent americium, curium, plus multiple lanthanide ions (La 3+ , Nd 3+ , Sm 3+ , Eu 3+ , Yb 3+ , and Lu 3+ ), in the presence of the model POM ligand PW 11 O 39 7– was elucidated and revealed the concurrent formation of two stable complexes, [M III (PW 11 O 39 )(H 2 O) x ] 4– and [M III (PW 11 O 39 ) 2 ] 11– . Interconversion reaction constants, reaction enthalpies, and reaction entropies were derived from the NMR data. The NMR results also provide experimental evidence of the weakly paramagnetic nature of the Am 3+ and Cm 3+ ions in solution. Furthermore, the study reveals a previously unnoticed periodicity break along the f-element series with the reversal of T 1 relaxation times of the 1:1 and 1:2 complexes and the preferential formation of the long T 1 species for the early lanthanides versus the short T 1 species for the late lanthanides, americium, and curium. Furthermore, given the broad variety of POM ligands that exist, with many of them containing NMR-active nuclei, the combined POM–NMR approach reported here opens a new avenue to investigate difficult-to-study elements such as heavy actinides and other radionuclides.

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

DFT-Based Calculation of Molecular Hyperpolarizability and SFG Intensity of Symmetric and Asymmetric Stretch Modes of Alkyl Groups

Vibrational sum frequency generation (SFG) spectroscopy has been extensively used for obtaining structural information of molecular functional groups at two-dimensional (2D) interfaces buried in the gas or liquid medium. Although the SFG experiment can be done elegantly, interpreting the measured intensity in terms of molecular orientation with respect to the lab coordinate is quite complicated. One of the main reasons is the difficulty of determining the hyperpolarizability tensors of even simple molecules that govern their SFG responses. The single-bond polarizability derivative (SBPD) model has been proposed to estimate the relative magnitude of SFG-active hyperpolarizability by assuming that the perturbation associated to each vibration is negligible. In this study, density functional theory (DFT) was used to calculate the polarizability and dipole derivative tensors of the CH 3 stretch mode of CH 3 I, CH 3 CH 2 I, CH 3 OH, CH 3 CH 2 OH. Then, the hyperpolarizability tensors of symmetric and asymmetric vibration modes were calculated considering the Boltzmann distribution of representative conformers, which allowed theoretical calculation of their SFG intensities at all polarization combinations as a function of the tilt angle of the CH 3 group with respect to the surface normal direction. Then the ratios of the calculated SFG intensities for the CH 3 peaks used in experimental studies for the CH 3 tilt angle determination were compared. This comparison clearly showed the effect of vibrational coupling among neighboring functional groups. As a result, this study presents new parameters that can be used in determining the average tilt angle of the CH 3 group at the 2D interface with SFG measurements as well as limitations of the method.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

X-ray Emission Spectroscopy of Single Protein Crystals Yields Insights into Heme Enzyme Intermediates

Enzyme reactivity is often enhanced by changes in oxidation state, spin state, and metal-ligand covalency of associated metallocofactors. The development of spectroscopic methods for studying these processes coincidentally with structural rearrangements is essential for elucidating metalloenzyme mechanisms. Herein, we demonstrate the feasibility of collecting X-ray emission spectra of metalloenzyme crystals at a third-generation synchrotron source. In particular, we report the development of a von Hamos spectrometer for the collection of Fe Kβ emission optimized for analysis of dilute biological samples. Here, we further showcase its application in crystals of the immunosuppressive heme-dependent enzyme indoleamine 2,3-dioxygenase. Spectra from protein crystals in different states were compared with relevant reference compounds. Complementary density functional calculations assessing covalency support our spectroscopic analysis and identify active site conformations that correlate to high- and low-spin states. These experiments validate the suitability of an X-ray emission approach for determining spin states of previously uncharacterized metalloenzyme reaction intermediates.

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

Earth-Abundant Manganese Nitride Catalysts for Mild-Condition Ammonia Synthesis

Developing advanced catalytic materials for mild-condition ammonia (NH 3 ) synthesis is essential for improving the energy efficiency of the industrial Haber-Bosch process. Here, in this study, we report a ζ-phase manganese nitride (MnN 0.43 ) catalyst for low-temperature NH 3 synthesis. The as-synthesized MnN 0.43 catalyst is protected by a carbon shell, allowing for the storage and processing of the air-sensitive metal nitride under ambient conditions. After activation in situ, the MnN 0.43 catalyst exhibits high activity for NH 3 synthesis at 250–350 °C, surpassing the conventional noble metal based Ru/MgO catalyst. A combination of kinetic, chemisorption, isotope labeling and computational studies indicate that a nitrogen vacancy-mediated associative mechanism accounts for the catalytic enhancements. Our work highlights the great potential of earth-abundant transition metal nitrides for catalyzing mild-condition NH 3 synthesis.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A Pair of 2D Quantum Liquids: Investigating the Phase Behavior of Indirect Excitons

Long-lived indirect excitons (IXs) exhibit a rich phase diagram, including a Bose–Einstein condensate (BEC), a Wigner crystal, and other exotic phases. Recent experiments have hinted at a “classical” liquid of IXs above the BEC transition. To uncover the nature of this phase, we use a broad range of theoretical tools and find no evidence of a driving force toward classical condensation. Instead, we attribute the condensed phase to a quantum electron–hole liquid (EHL), first proposed by Keldysh for direct excitons. Taking into account the association of free carriers into bound excitons, we study the phase equilibrium between a gas of excitons, a gas of free carriers, and an EHL for a wide range of electron–hole separations, temperatures, densities, and mass ratios. Our results agree reasonably well with recent measurements of GaAs/AlGaAs coupled quantum wells.

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

CO Adsorbates Induced Framework-Associated Low-Valence Co δ+ Sites in Co-ZSM-5 for Ethane Dehydrogenation

The dynamic structural evolution of heterogeneous catalysts is a ubiquitous phenomenon that has attracted a lot of interest. Catalyst reconstruction can occur after appropriate pretreatment, resulting in more efficient active catalysts, which is an attractive but challenging issue. Here, we reveal a CO activation strategy that controls the microenvironment of the Co sites in the high-silica Co-ZSM-5 catalyst (denoted as 0.50Co-Z5(340)), resulting in three times higher initial conversion and superior regeneration durability in the ethane dehydrogenation reaction compared to the same catalyst without CO pretreatment. In situ spectroscopy and metadynamics simulations reveal that the Co 2+ sites in 0.50Co-Z5(340) dislodge from the framework and move toward the nearby Brønsted acid sites, forming framework-associated low-valence Co δ+ species. Mechanistic studies indicate that the Co δ+ species catalyze ethane C–H bond cleavage via an oxidative addition mechanism, and ethylene is produced simultaneously with H* coupling (direct pathway). The promoted C–H bond activation and facile ethylene desorption explain the superior ethane dehydrogenation performance of the herein CO preactivated 0.50Co-Z5(340) catalyst.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Uncertainty quantification for Bayesian active learning in rupture life prediction of ferritic steels

Abstract Three probabilistic methodologies are developed for predicting the long-term creep rupture life of 9–12 wt%Cr ferritic-martensitic steels using their chemical and processing parameters. The framework developed in this research strives to simultaneously make efficient inference along with associated risk, i.e., the uncertainty of estimation. The study highlights the limitations of applying probabilistic machine learning to model creep life and provides suggestions as to how this might be alleviated to make an efficient and accurate model with the evaluation of epistemic uncertainty of each prediction. Based on extensive experimentation, Gaussian Process Regression yielded more accurate inference ( $$Pearson\;correlation\;coefficent> 0.95$$ P e a r s o n c o r r e l a t i o n c o e f f i c e n t > 0.95 for the holdout test set) in addition to meaningful uncertainty estimate (i.e., coverage ranges from 94 to 98% for the test set) as compared to quantile regression and natural gradient boosting algorithm. Furthermore, the possibility of an active learning framework to iteratively explore the material space intelligently was demonstrated by simulating the experimental data collection process. This framework can be subsequently deployed to improve model performance or to explore new alloy domains with minimal experimental effort.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2023

Tropical lacustrine sediment microbial community response to an extreme El Niño event

Salinity can influence microbial communities and related functional groups in lacustrine sediments, but few studies have examined temporal variability in salinity and associated changes in lacustrine microbial communities and functional groups. To better understand how microbial communities and functional groups respond to salinity, we examined geochemistry and functional gene amplicon sequence data collected from 13 lakes located in Kiritimati, Republic of Kiribati (2° N, 157° W) in July 2014 and June 2019, dates which bracket the very large El Niño event of 2015–2016 and a period of extremely high precipitation rates. Lake water salinity values in 2019 were significantly reduced and covaried with ecological distances between microbial samples. Specifically, phylum- and family-level results indicate that more halophilic microorganisms occurred in 2014 samples, whereas more mesohaline, marine, or halotolerant microorganisms were detected in 2019 samples. Functional Annotation of Prokaryotic Taxa (FAPROTAX) and functional gene results (nifH, nrfA, aprA) suggest that salinity influences the relative abundance of key functional groups (chemoheterotrophs, phototrophs, nitrogen fixers, denitrifiers, sulfate reducers), as well as the microbial diversity within functional groups. Accordingly, we conclude that microbial community and functional gene groups in the lacustrine sediments of Kiritimati show dynamic changes and adaptations to the fluctuations in salinity driven by the El Niño-Southern Oscillation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Cecal microbiota composition differs under normal and high ambient temperatures in genetically distinct chicken lines

Modern broilers, selected for high growth rate, are more susceptible to heat stress (HS) as compared to their ancestral jungle fowl (JF). HS affects epithelia barrier integrity, which is associated with gut microbiota. The aim of this study was to determine the effect of HS on the cecal luminal (CeL) and cecal mucosal (CeM) microbiota in JF and three broiler populations: Athens Canadian Random Bred (ACRB), 1995 Random Bred (L1995), and Modern Random Bred (L2015). Broiler chicks were subjected to thermoneutral TN (24 °C) or chronic cyclic HS (8 h/day, 36 °C) condition from day 29 until day 56. HS affected richness in CeL microbiota in a line-dependent manner, decreasing richness in slow-growing JF and ACRB lines, while increasing richness in faster-growing L1995 and L2015. Microbiota were distinct between HS and TN conditions in CeL microbiota of all four lines and in CeM microbiota of L2015. Certain bacterial genera were also affected in a line-dependent manner, with HS tending to increase relative abundance in CeL microbiota of slow-growing lines, while decreases were common in fast-growing lines. Predictive functional analysis suggested a greater impact of HS on metabolic pathways in L2015 compared to other lines.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

National serosurvey and risk mapping reveal widespread distribution of Coxiella burnetii in Kenya

Coxiella burnetii, the causative agent of Q fever, is an emerging pathogen that has the potential to cause severe chronic infections in animals and humans worldwide. The detrimental impact on public health is projected to be higher in the low- and middle-income countries given their lower capacity to sustain effective surveillance and response measures. We implemented a national serosurvey of cattle in Kenya to map the spatial distribution of the pathogen. The study used serum samples that were collected from randomly selected cattle in different ago-ecological zones across the country. These samples were screened for the pathogen using PrioCHECK Ruminant Q Fever AB Plate ELISA kit. The laboratory findings were analyzed using INLA package to identify risk factors for C. burnetii exposure from herd- and animal-level factors, area, and bioclimatic datasets accessed from online databases. A total of 6,593 cattle were recruited for the study; of these, 7.9% (95% CI; 7.2–8.5) were seropositive. Outputs from the multivariable analysis revealed that the animal age and some of the geographical variables including wind speed, area under shrubs and “petric calcisols” type of soil were significantly associated with C. burnetii seropositivity. Being a calf, weaner or subadult was associated with lower odds of exposure compared to being an adult by 0.24 (credibility interval: 2.5% and 97.5%), 0.41 (0.30–0.55) and 0.51 (0.38–0.69), respectively. In addition, a unit increase in the wind speed increased the odds of C. burnetii seropositivity by 1.27 (1.05–1.52) while an increase on the land area under shrubs was associated with lower odds of exposure (0.67 [0.47–0.69]). The effect of petric calcisols was non-linear; an increase of the land area with this soil type was associated with an exponential increase in C. burnetii seropositivity. This study provides new data on C. burnetii seroprevalence, information of its risk factors and a prevalence map that can be used for C. burnetii risk surveillance and control. The identification of environmental risk factors for C. burnetii exposure, and the increasing awareness of the zoonotic potential of the pathogen, calls for the need to enhance the existing collaborations for the surveillance and control of C. burnetii in line with the One Health framework. The evidence generated on the potential role of environmental factors can also be used to design nature-based interventions, such as replacement of vegetation in denuded areas, to reduce potential for the aerosolization of the pathogen. Livestock vaccination in the hotspots would also reduce animal infections and hence the contamination of the environment.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Life cycle greenhouse gas emissions and carbon intensity of U.S. fuel use and projection for the next 10 years-based on built capacity and expansion plans

The U.S. Inflation Reduction Act of 2022 supports biofuel production expansion through the 45Z clean fuel production tax credit, replacing previous 40A and 40B credits. This follows on the Renewable Fuel Standard from the Energy Policy Act of 2005 and its expansion in 2007. States like California, Oregon, and Washington also offer clean fuel credits. Meanwhile, federal agencies, including the U.S. Department of Energy, have advanced alternative fuel technologies through research and development funding. The surging interest in the biofuel industry has spurred the demand for biofuel supplies in the markets, although achieving profitability for advanced biofuels and low-carbon e-fuels remains challenging. This study aims to track U.S. alternative fuel production capacity expansion plans over the next 10 years and estimate impacts on greenhouse gas (GHG) emissions. By tracking built capacity and industry announcements of planned expansion, this study complements other studies which use models to predict changes in energy technologies and the associated GHG implications. Modeled projections of future technologies are often criticized for over or underestimating the cost and potential role of new technologies. The study focuses on sustainable aviation fuel, renewable diesel, ethanol, biodiesel, and renewable natural gas. Using facility-level data, we conducted a bottom-up analysis linking biofuel production pathways with corresponding pathways and parameterizations in the Argonne R&D GREET model. Results indicate that biofuel capacity could reach 3.8 exajoules in 2035, potentially reducing U.S. GHG emissions by 179 million tonnes, including the full life cycle. This corresponds to a 20% reduction in transportation and 5% in industry sector emissions by 2035, or a 3.6% reduction in economy-wide emissions. Overall, this study shows that while biofuel production capacity in the U.S. is expanding, the capacities remain limited compared to fuel demand. Uncertainty regarding the durability and extension of incentives may be dampening the pace of growth. Meanwhile, demonstrating the commercial potential for alternative fuels and climbing the learning curve for new technologies could lead to an increased pace of expansion in later years. This study offers insights for bioenergy stakeholders, highlighting biofuel technologies' contribution to U.S. energy system and emissions reduction over time based on producers' plans.

Biofuel Producers↗
DOE OSTI2023

Astronomical image time series classification using CONVolutional attENTION (ConvEntion)

Aims: The treatment of astronomical image time series has won increasing attention in recent years. Indeed, numerous surveys following up on transient objects are in progress or under construction, such as the Vera C. Rubin Observatory Legacy Survey for Space and Time (LSST), which is poised to produce huge amounts of these time series. The associated scientific topics are extensive, ranging from the study of objects in our galaxy to the observation of the most distant supernovae for measuring the expansion of the universe. With such a large amount of data available, the need for robust automatic tools to detect and classify celestial objects is growing steadily. Methods: This study is based on the assumption that astronomical images contain more information than light curves. In this paper, we propose a novel approach based on deep learning for classifying different types of space objects directly using images. We named our approach ConvEntion, which stands for CONVolutional attENTION. It is based on convolutions and transformers, which are new approaches for the treatment of astronomical image time series. Our solution integrates spatio-temporal features and can be applied to various types of image datasets with any number of bands. Results: In this work, we solved various problems the datasets tend to suffer from and we present new results for classifications using astronomical image time series with an increase in accuracy of 13%, compared to state-of-the-art approaches that use image time series, and a 12% increase, compared to approaches that use light curves.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Detection and characterization of detached tidal dwarf galaxies

Tidal interactions between galaxies often give rise to tidal tails, which can harbor concentrations of stars and interstellar gas resembling dwarf galaxies. Some of these tidal dwarf galaxies (TDGs) have the potential to detach from their parent galaxies and become independent entities, but their long-term survival is uncertain. In this study, we conducted a search for detached TDGs associated with a sample of 39 interacting galaxy pairs in the local Universe using infrared, ultraviolet, and optical images. We employed IR colors and UV/optical/IR spectral energy distributions to identify potential interlopers, such as foreground stars or background quasars. Through spectroscopic observations using the Boller and Chivens spectrograph at San Pedro Mártir Observatory, we confirmed that six candidate TDGs are at the same redshift as their putative parent galaxy pairs. We identified and measured emission lines in the optical spectra and calculated nebular oxygen abundances, which range from log(O/H) = 8.10 ± 0.01 to 8.51 ± 0.02. We have serendipitously discovered an additional detached TDG candidate in Arp72 using available spectra from SDSS. Utilizing the photometric data and the CIGALE code for stellar population and dust emission fitting, we derived the stellar masses, stellar population ages, and stellar metallicities for these detached TDGs. Compared to standard mass-metallicity relations for dwarf galaxies, five of the seven candidates have higher than expected metallicities, confirming their tidal origins. One of the seven candidates remains unclear due to large uncertainties in metallicity, and another has stellar and nebular metallicities compatible with those of a preexisting dwarf galaxy. The latter object is relatively compact in the optical relative to its stellar mass, in contrast to the other candidate TDGs, which have large diameters for their stellar masses compared to most dwarf galaxies. The derived stellar population ages range from 100 Myr to 900 Myr, while the inferred stellar masses are between 2 × 10 6 M ⊙ and 8 × 10 7 M ⊙ . Four of the six TDGs are associated with the gas-rich M51-like pair Arp 72, one TDG is associated with a second M51-like pair Arp 86, and another is associated with Arp 65, an approximately equal mass pair. In spite of the relatively low stellar masses of these TDGs, they have survived for at least 100–900 Myrs, suggesting that they are stable and in dynamical equilibrium. We conclude that encounters with a relatively low-mass companion (1/10th–1/4th of the mass of the primary) can also produce long-lasting TDGs.

Astronomy & Astrophysics↗
DOE OSTI2024

High energy electron diffraction instrument with tunable camera length

Ultrafast electron diffraction (UED) stands as a powerful technique for real-time observation of structural dynamics at the atomic level. In recent years, the use of MeV electrons from radio frequency guns has been widely adopted to take advantage of the relativistic suppression of the space charge effects that otherwise limit the temporal resolution of the technique. Nevertheless, there is not a clear choice for the optimal energy for a UED instrument. Scaling to beam energies higher than a few MeV does pose significant technical challenges, mainly related to the inherent increase in diffraction camera length associated with the smaller Bragg angles. In this study, we report a solution by using a compact post-sample magnetic optical system to magnify the diffraction pattern from a crystal Au sample illuminated by an 8.2 MeV electron beam. Our method employs, as one of the lenses of the optical system, a triplet of compact, high field gradients (>500 T/m), small-gap (3.5 mm) Halbach permanent magnet quadrupoles. Shifting the relative position of the quadrupoles, we demonstrate tuning the magnification by more than a factor of two, a 6× improvement in camera length, and reciprocal space resolution better than 0.1 Å –1 in agreement with beam transport simulations.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Optimized utilization of COMB3 reactive potentials in LAMMPS

An investigation to optimize the application of the third-generation charge optimized many-body (COMB3) interatomic potential and associated input parameters was carried out through the study of solid–liquid interactions in classical molecular dynamics simulations. The rates of these molecular interactions are understood through the wetting rates of water nano-droplets on a bare copper (111) surface. Implementing the Langevin thermostat, the influence of simulation time step, the number of atoms in the system, the frequency at which charge equilibration is performed, and the temperature relaxation rate are all examined. The results indicate that time steps of 0.4 fs are possible when using longer relaxation times for the system temperature, which is almost double the typical time step used for reactive potentials. Additionally, the use of the charge equilibration allows for a fewer atomic layers to be used in the Cu slab. In addition, charge equilibrium schemes do not need to be performed every time step to ensure accurate charge transfer. Interestingly, the rate of wetting for the nanodroplets is dominantly dependent on the temperature relaxation time, which is predicted to significantly change the viscosity of the water droplets. This work provides a pathway for optimizing simulations using the COMB3 reactive interatomic potential.

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

Spatial description of dislocation nucleation in the shock response of single-crystal aluminum

Nonequilibrium molecular dynamics simulations of shock loaded single-crystal Al in the $\langle$100$\rangle$, $\langle$110$\rangle$, $\langle$111$\rangle$, and $\langle$123$\rangle$ orientations are conducted to study elastic and plastic shockwave formation and details associated with dislocation activity. A computer vision-based approach is implemented to capture the presence of dislocations and describe their spatial characteristics in the zone of nucleation behind the propagating shockwave. The methodology developed relies on the sequences of images extracted during shock loading that show dislocation activity within a cross section of the sample. Results reveal that the spacing between activated slip systems is orientation dependent and exhibits a modest reduction for the $\langle$100$\rangle$ and $\langle$111$\rangle$ orientations as shock pressure increases. Comparisons are made to existing theoretical models. Such relationships between shock pressure and dislocation activity, extracted from molecular dynamics simulations, can be used to inform higher length scale simulations or modeling of dislocation-based plasticity during shock.

36 MATERIALS SCIENCE↗