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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Machine-Learned Force Field for Molecular Dynamics Simulations of Nonequilibrium Ammonia Synthesis on Iron Catalysts

Ammonia (NH 3 ) is one of the most important industrial chemicals. The conventional NH 3 synthesis method-the Haber–Bosch process-converts atmospheric nitrogen (N 2 ) into NH 3 using H 2 with an iron (Fe) catalyst. However, this process requires high pressures (100–200 atm) and temperatures (700–800 K) near thermal equilibrium. Recently, Fe-based nanocatalysts have been reported to produce promising NH 3 yields under atmospheric pressures and temperature-modulated nonequilibrium conditions. Understanding the mechanism of nonequilibrium catalysis with programmed temperature variation could help to optimize this fully electrified and less energy-intensive process. Although reactive molecular dynamics (RMD) simulations can be a useful tool to model nonequilibrium catalytic processes, they require the development of accurate force fields (i.e., interatomic potentials). Here, we present a machine-learned (ML) force field within the Deep Potential MD (DPMD) framework, trained using periodic density functional theory (DFT) calculations, to model NH 3 synthesis on Fe catalysts with various surface adsorbates such as *N, *H, *N 2 , *H 2 , *NH, *NH 2 , and *NH 3 . Here, we generated the DFT data from static models of elementary reactions on the most stable (110) surface of body-centered cubic Fe, which then were augmented by data from constant number of particles–volume–temperature (NVT) DFT-MD trajectories at various temperatures. Finally, we utilized the fully optimized ML force field to investigate reaction dynamics at an Fe(110) surface at linearly increasing temperatures using NVT-DPMD simulations. Our simulations indicate that pulsed temperature ramping could prove favorable for NH3 synthesis. For example, we conducted ramping under multiple sets of conditions: (i) from 900 to 1200 K over periods of 0.1–0.3 ns for Fe surfaces precovered with N or NH along with H; and (ii) from 300 to 600 K over 0.1–0.3 ns for Fe surfaces precovered with NH 3 . While our simulations so far are limited to short time scales (very rapid heating), these observations shed light on the mechanism of the high NH 3 synthesis rate achieved in a novel temperature-modulated nonequilibrium catalytic reactor using pulsed heating and cooling.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Understanding Adsorption and Reactions at Aqueous Oxide Interfaces with Neural Network Potential Molecular Dynamics

Chemical processes at metal oxide−water interfaces are of central importance in geochemistry, biology, and energy technologies. A better understanding of these processes would allow us to make a significant step toward optimizing and controlling them, which could in turn lead to broader impacts. Computational modeling is indispensable to accomplishing this task because complexity and disorder often make it difficult to extract atomistic information from experiments. Balancing computational cost and accuracy, simulation schemes based on efficient machine learning representations of the potential energy surface (PES) predicted by ab initio calculations have become increasingly popular over the past decade. In particular, several studies have demonstrated the ability of machine learning models to accurately reproduce the complex ab initio PESs of aqueous oxide interfaces, allowing simulations of systems and processes that are not accessible with ab initio methods. In this Account, we review our recent efforts to understand adsorption processes and reactions at aqueous oxide interfaces using deep potential molecular dynamics (DPMD), a simulation scheme employing deep neural networks (DNNs), which has proven to be quite successful in accurately describing many different systems in the condensed phase. After summarizing the DPMD methodology, we first review our work on the acid−base chemistry of oxide surfaces in contact with water, a fundamental characteristic that controls proton transfer and surface charge at the interface. We focus on the aqueous interface of rutile IrO 2 , an oxide material thus far considered the best catalyst for the oxygen evolution reaction (OER). We show that this interface is characterized by a large fraction of dissociated water and a strong Brønsted acidity of the surface sites, in good agreement with the experimentally measured value of the point of zero proton charge. In our second example, we investigate how the adsorption of organic species from ambient air or water affects the structure and wettability of the aqueous interfaces of TiO 2 , a prototypical photocatalytic material. This is a question that is relevant to understanding the UV-induced hydrophilicity of TiO 2 surfaces, a property at the basis of self-cleaning windows and related applications. Specifically focusing on formic and acetic acids, the two most common atmospheric organic acids, our simulations reveal that these acids control the wettability of TiO 2 largely through acid−base chemistry at the interface rather than chemisorption on the oxide surface, a finding that could help improve the design of self-cleaning surfaces and photocatalytic devices. Finally, we review our recent study of methanol at TiO 2 −water interfaces, a system whose interest is largely motivated by the role of methanol in enhancing photocatalytic hydrogen evolution on TiO 2 . Our simulations provide mechanistic insights into the coupled roles of the organic adsorbate and water at the TiO 2 interface, with implications for how methanol enhances the activity of H 2 evolution.

adsorption↗
DOE OSTI2020

A new hydrous iron oxide phase stable at mid-mantle pressures

The amount of hydrogen stored in the Earth's interior is important for a range of issues, including the volatile incorporation during the Earth formation and the co-evolution of the atmosphere, the hydrosphere, and the interior. Recent experiments found titanium bearing ε-FeOOH in a hydrous basaltic system at 12–19 GPa and 1300 K. Pyrite-type FeOOH was found to be stable at pressures higher than 80 GPa. These discoveries suggest possible hydrogen storage in the mantle transition zone and in the mantle below 1800 km depths, respectively. However, it remains uncertain how the potential deep hydrogen storage can be connected to the shallower storage. Here, we report a new hydrous iron oxide (η-Fe 12 O 18+x / 2 H x , x ≈ 2) stable at pressures between the stability fields of the ε- and the pyrite-type FeOOH. Our experiment also shows that the new η phase can exist together with the major lower mantle minerals including bridgmanite and periclase, making it an important hydrogen-bearing phase in the Earth's deep interior. Because of its limited H 2 O storage capacity, which is less than 1/6 of the storage capacity of the pyrite-type phase and the ε phase, the stability of the η phase would result in H 2 O loss during water transport in the mid mantle and therefore limit the amount of H 2 O potentially stored in the Fe–O–H system of the lower mantle. As a result, the large channel in the crystal structure of the η phase could provide potential storage sites for other volatile elements in the deep mantle.

58 GEOSCIENCES↗
DOE OSTI2023

First-Principles-Based Machine Learning Models for Phase Behavior and Transport Properties of CO 2

In this work, we construct distinct first-principles-based machine-learning models of CO 2 , reproducing the potential energy surface of the PBE-D3, BLYP-D3, SCAN and SCAN-rvv10 approximations of density functional theory. Here we employ the Deep Potential methodology to develop the models and consequently achieve a significant computational efficiency over ab initio molecular dynamics (AIMD) that allows for larger system sizes and time scales to be explored. Although our models are trained only with liquid phase configurations, they are able to simulate a stable interfacial system and predict vapor-liquid equilibrium properties, in good agreement with results from the literature. Because of the computational efficiency of the models, we are also able to obtain transport properties, such as viscosity and diffusion coefficients. We find that the SCAN-based model presents a temperature shift in the position of the critical point, while the SCAN-rvv10-based model shows improvement but still exhibits a temperature shift that remains approximately constant for all properties investigated in this work. We find that the BLYP-D3-based model generally performs better for liquid phase and vapor-liquid equilibrium properties, but the PBE-D3-based model is better suited for predicting transport properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1993

Calculations on the competition between association and reaction for C3H(+) + H2

The ab initio results presently obtained for the potential energy surface of competing associative and reactive ion-molecule processes of the reactants H2 and C3H(+) show that the linear ion is able to directly access the deep potential well of the propargyl ion H2CCCH(+). Using the minimum energy potential pathway and properties of the stationary point structures determined via the ab initio methods, calculation results are obtained for both the association-rate coefficient for C3H3(+) production as a function of density, and the branching ratio between the propargyl and cyclic structures of the ion.

Maluendes, Sergio A.↗
NASA NTRS1990

Potential, velocity, and density fields from redshift-distance samples: Application - Cosmography within 6000 kilometers per second

A potential flow reconstruction algorithm has been applied to the real universe to reconstruct the three-dimensional potential, velocity, and mass density fields smoothed on large scales. The results are shown as maps of these fields, revealing the three-dimensional structure within 6000 km/s distance from the Local Group. The dominant structure is an extended deep potential well in the Hydra-Centaurus region, stretching across the Galactic plane toward Pavo, broadly confirming the Great Attractor (GA) model of Lynden-Bell et al. (1988). The Local Supercluster appears to be an extended ridge on the near flank of the GA, proceeding through the Virgo Southern Extension to the Virgo and Ursa Major clusters. The Virgo cluster and the Local Group are both falling toward the bottom of the GA potential well with peculiar velocities of 658 + or - 121 km/s and 565 + or - 125 km/s, respectively.

Bertschinger, Edmund↗
DOE OSTI2023

Neural Network Water Model Based on the MB-Pol Many-Body Potential

The MB-pol many-body potential accurately predicts many properties of water, including cluster, liquid phase, and vapor–liquid equilibrium properties, but its high computational cost can make applying it in large-scale simulations quite challenging. Here, in order to address this limitation, we developed a “deep potential” neural network (DPMD) model based on the MB-pol potential for water. We find that a DPMD model trained on mostly liquid configurations yields a good description of the bulk liquid phase but severely underpredicts vapor–liquid coexistence densities. By contrast, adding cluster configurations to the neural network training set leads to a good agreement for the vapor coexistence densities. Liquid phase densities under supercooled conditions are also represented well, even though they were not included in the training set. These results confirm that neural network models can combine accuracy and transferability if sufficient attention is given to the construction of a representative training set for the target system.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2014

Advancing Navigation, Timing, and Science with the Deep Space Atomic Clock

NASA's Deep Space Atomic Clock mission is developing a small, highly stable mercury ion atomic clock with an Allan deviation of at most 1e-14 at one day, and with current estimates near 3e-15. This stability enables one-way radiometric tracking data with accuracy equivalent to and, in certain conditions, better than current two-way deep space tracking data; allowing a shift to a more efficient and flexible one-way deep space navigation architecture. DSAC-enabled one-way tracking will benefit navigation and radio science by increasing the quantity and quality of tracking data. Additionally, DSAC would be a key component to fully-autonomous onboard radio navigation useful for time-sensitive situations. Potential deep space applications of DSAC are presented, including orbit determination of a Mars orbiter and gravity science on a Europa flyby mission.

radio science↗
DOE OSTI2024

Quasi-Classical Trajectory Calculation of Rate Constants Using an Ab Initio Trained Machine Learning Model (aML-MD) with Multifidelity Data

Machine learning (ML) provides a great opportunity for the construction of models with improved accuracy in classical molecular dynamics (MD). However, the accuracy of a ML trained model is limited by the quality and quantity of the training data. Generating large sets of accurate ab initio training data can require significant computational resources. Furthermore, inconsistent or incompatible data with different accuracies obtained using different methods may lead to biased or unreliable ML models that do not accurately represent the underlying physics. Recently, transfer learning showed its potential for avoiding these problems as well as for improving the accuracy, efficiency, and generalization of ML models using multifidelity data. In this work, ab initio trained ML-based MD (aML-MD) models are developed through transfer learning using DFT and multireference data from multiple sources with varying accuracy within the Deep Potential MD framework. Further, the accuracy of the force field is demonstrated by calculating rate constants for the H + HO 2 → H 2 + 3 O 2 reaction using quasi-classical trajectories. We show that the aML-MD model with transfer learning can accurately predict the rate constants while reducing the computational cost by more than five times compared to the use of more expensive quantum chemistry training data sets. Hence, the aML-MD model with transfer learning shows great potential in using multifidelity data to reduce the computational cost involved in generating the training set for these potentials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2021

Strategies for the Design and Operation of Resilient Extraterrestrial Habitats

An Earth-independent permanent extraterrestrial habitat system must function as intended under continuous disruptive conditions, and with significantly limited Earth support and extended uncrewed periods. Designing for the demands that extreme environments such as wild temperature fluctuations, galactic cosmic rays, destructive dust, meteoroid impacts (direct or indirect), vibrations, and solar particle events, will place on long-term deep space habitats represents one of the greatest challenges in this endeavor. This context necessitates that we establish the know-how and technologies to build habitat systems that are resilient. Resilience is not simply robustness or redundancy: it is a system property that accounts for both anticipated and unanticipated disruptions via the design choices and maintenance processes and adapts to them in operation. We currently lack the frameworks and technologies needed to achieve a high level of resilience in a habitat system. The Resilient ExtraTerrestrial Habitats Institute (RETHi) has the mission of leveraging existing novel technologies to provide situational awareness and autonomy to enable the design of habitats that are able to adapt, absorb and rapidly recover from expected and unexpected disruptions. We are establishing both fully virtual and coupled physical-virtual simulation capabilities that will enable us to explore a wide range of potential deep space SmartHab configurations and operating modes.

Space habitats↗
NASA NTRS2021

Strategies for the Design and Operation of Resilient Extraterrestrial Habitats

An Earth-independent permanent extraterrestrial habitat system must function as intended under continuous disruptive conditions, and with significantly limited Earth support and extended uncrewed periods. Designing for the demands that extreme environments such as wild temperature fluctuations, galactic cosmic rays, destructive dust, meteoroid impacts (direct or indirect), vibrations, and solar particle events, will place on long-term deep space habitats represents one of the greatest challenges in this endeavor. This context necessitates that we establish the know-how and technologies to build habitat systems that are resilient. Resilience is not simply robustness or redundancy: it is a system property that accounts for both anticipated and unanticipated disruptions via the design choices and maintenance processes, and adapts to them in operation. We currently lack the frameworks and technologies needed to achieve a high level of resilience in a habitat system. The Resilient Extra Terrestrial Habitats Institute (RETHi) has the mission of leveraging existing novel technologies to provide situational awareness and autonomy to enable the design of habitats that are able to adapt, absorb and rapidly recover from expected and unexpected disruptions. We are establishing both fully virtual and coupled physical-virtual simulation capabilities that will enable us to explore a wide range of potential deep space Smart Hab configurations and operating modes.

Space habitats↗
DOE OSTI2025

Dynamic Metal–Support Interaction Dictates Cu Nanoparticle Sintering on Al 2 O 3 Surfaces

Nanoparticle sintering remains a critical challenge in heterogeneous catalysis. In this work, we present a unified deep potential (DP) model based on the Perdew–Burke–Ernzerhof approximation of density functional theory for Cu nanoparticles on three Al 2 O 3 surfaces (γ-Al 2 O 3 (100), γ-Al 2 O 3 (110), and α-Al 2 O 3 (0001)). Using DP-accelerated simulations, we reveal that the nanoparticle size-mobility relationship strongly depends on the supporting surface. The diffusion of nanoparticles on the two γ-Al 2 O 3 surfaces is almost independent of the size of the nanoparticle, while the diffusion on α-Al 2 O 3 (0001) decreases rapidly with increasing size. Interestingly, nanoparticles with fewer than 55 atoms diffuse several times faster on α-Al 2 O 3 (0001) than on γ-Al 2 O 3 (100) at 800 K while expected to be more sluggish based on their larger binding energy at 0 K. The diffusion on α-Al 2 O 3 (0001) is facilitated by dynamic metal–support interaction (MSI), where Al atoms move out of the surface plane to optimize contact with the nanoparticle and relax back to the plane as the nanoparticle moves away. In contrast, the MSI on γ-Al 2 O 3 (100) and on γ-Al 2 O 3 (110) is dominated by more stable and directional Cu–O bonds, consistent with the limited diffusion observed on these surfaces. Our extended MD simulations provide insight into the sintering processes, showing that the dispersity of the nanoparticles strongly influences the coalescence driven by nanoparticle diffusion. We observed that the coalescence of Cu 13 nanoparticles on α-Al 2 O 3 (0001) can occur in a short time (10 ns) at 800 K even with an initial internanoparticle distance increased to 3 nm, while the coalescence on the two γ-Al 2 O 3 surfaces are inhibited significantly by increasing the initial internanoparticle distance. These findings demonstrate that the dynamics of the supporting surface is crucial to understanding the sintering mechanism and offer guidance for designing sinter-resistant catalysts by engineering the support morphology.

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

Domain wall motion across microstructural features in polycrystalline ferroelectric films

This paper describes the effect of microstructural features such as grain boundaries and triple points on the pinning of domain wall motion in perovskite Pb(Zr 0.52 Ti 0.48 )O 3 (PZT) films on polycrystalline SrRuO 3 /SrTiO 3 substrates. Here, spatial variability in the collective domain wall dynamics was assessed using non-linearity mapping via Band Excitation Piezoresponse Force Microscopy (BE-PFM). Collocating the non-linearity maps with triple point locations (as visualized by EBSD) allowed for exploration of the effects that local microstructure (e.g., grain boundary) have on domain wall motion. It was found that the extrinsic behavior varied with both the misorientation angle and the proximity to the grain boundary. The width of influence of individual grain boundaries on the motion of domain walls was a function of the character of the grain boundary; random grain boundaries exhibit deeper minima in α d /d 33,initial and larger widths of influence (up to 905 nm) compared to coincident site lattice (CSL) boundaries (up to 572 nm). Additionally, triple points containing larger numbers of random boundaries exhibited non-Rayleigh behavior to greater distances, suggesting that the triple point provides either a deep potential minimum or a region where domain wall motion is unfavorable.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Dynamic Carrier Modulation via Nonlinear Acoustoelectric Transport in van der Waals Heterostructures

Dynamically manipulating carriers in van der Waals heterostructures could enable solid-state quantum simulators with tunable lattice parameters. A key requirement is the formation of deep potential wells to reliably trap excitations. Here, we report the observation of nonlinear acoustoelectric transport and dynamic carrier modulation in boron nitride-encapsulated graphene devices coupled to intense surface acoustic waves (SAWs) on LiNbO 3 substrates. SAWs generate strong acoustoelectric current densities ( J AE ), transitioning from linear to nonlinear regimes with increasing SAW intensity. In the nonlinear regime, periodic carrier (electrons, holes, or their mixtures) stripes emerge. Using counter-propagating SAWs, we create standing SAWs (SSAWs) to dynamically manipulate charge distributions without static gates. The saturation of J AE , attenuation transitions, and tunable resistance peaks confirms strong carrier localization. Finally, these results establish SAWs as a powerful tool for controlling carrier dynamics in two-dimensional (2D) materials, paving the way for the development of time-dependent quantum systems and acoustic lattices for quantum simulation.

2D materials↗
DOE OSTI2024

Ab initio generalized Langevin equation

We introduce a machine learning–based approach called ab initio generalized Langevin equation (AIGLE) to model the dynamics of slow collective variables (CVs) in materials and molecules. In this scheme, the parameters are learned from atomistic simulations based on ab initio quantum mechanical models. Force field, memory kernel, and noise generator are constructed in the context of the Mori–Zwanzig formalism, under the constraint of the fluctuation–dissipation theorem. Combined with deep potential molecular dynamics and electronic density functional theory, this approach opens the way to multiscale modeling in a variety of situations. Here, we demonstrate this capability with a study of two mesoscale processes in crystalline lead titanate, namely the field-driven dynamics of a planar ferroelectric domain wall, and the dynamics of an extensive lattice of coarse-grained electric dipoles. In the first case, AIGLE extends the reach of ab initio simulations to a regime of noise-driven motions not accessible to molecular dynamics. In the second case, AIGLE deals with an extensive set of CVs by adopting a local approximation for the memory kernel and retaining only short-range noise correlations. The scheme is computationally more efficient than molecular dynamics by several orders of magnitude and mimics the microscopic dynamics at low frequencies where it reproduces accurately the dominant far-infrared absorption frequency.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Kilopower Project: The KRUSTY Fission Power Experiment and Potential Missions

The Kilopower Project was initiated by NASA’s Space Technology Mission Directorate/Game Changing Development Program in fiscal year 2015 to demonstrate subsystem-level technology readiness of small space fission power in a relevant environment (Technology Readiness Level 5) for space science and human exploration power needs. The Kilopower Project centerpiece is the Kilowatt Reactor Using Stirling TechnologY (KRUSTY) test, which consists of the development and testing of a ground technology demonstrator of a 1-kW(electric)–class fission power system (FPS). The technologies to be developed and validated by KRUSTY are extensible to space FPSs from 1 to 10 kW(electric), which can enable modular surface FPSs for human exploration as well as higher-power future potential deep space science missions. The KRUSTY demonstration is cofunded by NASA and the U.S. Department of Energy National Nuclear Security Administration. The KRUSTY demonstration in the National Critical Experiment Research Center’s Device Assembly Facility was completed in the first quarter of 2018.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Thermoelectric Figure of Merit of a Superatomic Crystal Re 6 Se 8 I 2 Monolayer

Superatomic materials are newly emerging candidates for high-performance thermoelectric (TE) devices due to their intrinsic ultralow thermal conductivities. However, the low TE power factor becomes a huge obstacle to reaching the required the dimensionless figure of merit (ZT) values for practical applications. Here, motivated by the recently synthesized superatomic Re 6 Se 8 I 2 monolayer [J. Am. Chem. Soc. 144, 74 (2022)], we study its superior thermoelectric properties by using density functional theory combined with phonon Boltzmann transport theory and deep potential molecular dynamics. We show that the large mass and anharmonic Re-I bonds introduce strong phonon scattering and result in low lattice thermal conductivity of 1.20 W m -1 K -1 at 300 K, while the strong and harmonic Re-Se network ensures the high TE power factor of 4344 μW m -1 K -2 in b direction for n-type doping. This is an order of magnitude higher than that of other cluster-based materials. Due to the low thermal conductivity and high TE power factor, Re 6 Se 8 I 2 exhibits high ZT values of 1.20 (500 K) and 1.43 (900 K) with n-type doping along the b direction among all the cluster-based thermoelectric materials reported so far.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Deep neural network for the dielectric response of insulators

In this work, we introduce a deep neural network to model in a symmetry preserving way the environmental dependence of the centers of the electronic charge. The model learns from ab initio density functional theory, wherein the electronic centers are uniquely assigned by the maximally localized Wannier functions. When combined with the deep potential model of the atomic potential energy surface, the scheme predicts the dielectric response of insulators for trajectories inaccessible to direct ab initio simulation. The scheme is nonperturbative and can capture the response of a mutating chemical environment. We demonstrate the approach by calculating the infrared spectra of liquid water at standard conditions, and of ice under extreme pressure, when it transforms from a molecular to an ionic crystal.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗