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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Safe Physics-Informed Machine Learning for Dynamics and Control

This tutorial paper focuses on safe physics-informed machine learning in the context of dynamics and control, providing a comprehensive overview of how to integrate physical models and safety guarantees. As machine learning techniques enhance the modeling and control of complex dynamical systems, ensuring safety and stability remains a critical challenge, especially in safety-critical applications like autonomous vehicles, robotics, medical decision-making, and energy systems. We explore various approaches for embedding and ensuring safety constraints, including structural priors, Lyapunov and Control Barrier Functions, predictive control, projections, and robust optimization techniques. Additionally, we delve into methods for uncertainty quantification and safety verification, including reachability analysis and neural network verification tools, which help validate that control policies remain within safe operating bounds even in uncertain environments. The paper includes illustrative examples demonstrating the implementation aspects of safe learning frameworks that combine the strengths of data-driven approaches with the rigor of physical principles, offering a path toward the safe control of complex dynamical systems.

Drgona, Jan↗
DOE OSTI2024

Exploring Li-Ion Transport Properties of Li 3 TiCl 6 : A Machine Learning Molecular Dynamics Study

We performed large-scale molecular dynamics simulations based on a machine-learning force field (MLFF) to investigate the Li-ion transport mechanism in cation-disordered Li 3 TiCl 6 cathode at six different temperatures, ranging from 25°C to 100°C. In this work, deep neural network method and data generated by ab − initio molecular dynamics (AIMD) simulations were deployed to build a high-fidelity MLFF. Radial distribution functions, Li-ion mean square displacements (MSD), diffusion coefficients, ionic conductivity, activation energy, and crystallographic direction-dependent migration barriers were calculated and compared with corresponding AIMD and experimental data to benchmark the accuracy of the MLFF. From MSD analysis, we captured both the self and distinct parts of Li-ion dynamics. The latter reveals that the Li-ions are involved in anti-correlation motion that was rarely reported for solid-state materials. Similarly, the self and distinct parts of Li-ion dynamics were used to determine Haven’s ratio to describe the Li-ion transport mechanism in Li 3 TiCl 6 . Obtained trajectory from molecular dynamics infers that the Li-ion transportation is mainly through interstitial hopping which was confirmed by intra- and inter-layer Li-ion displacement with respect to simulation time. Ionic conductivity (1.06 mS/cm) and activation energy (0.29eV) calculated by our simulation are highly comparable with that of experimental values. Overall, the combination of machine-learning methods and AIMD simulations explains the intricate electrochemical properties of the Li 3 TiCl 6 cathode with remarkably reduced computational time. Thus, our work strongly suggests that the deep neural network-based MLFF could be a promising method for large-scale complex materials.

Selvaraj, Selva Chandrasekaran (ORCID:000000029023↗
DOE OSTI2026

Predictive ALEGRA Simulations of the Mach Wave Concept on the Z Machine

This high level presentation introduces the Sandia Z machine, dynamic materials properties experiments including the Mach Wave concept, and Alegra magnetohydrodynamic modeling for a technical audience. Alegra models are shown to be highly capable, with predictive capabilities limited only by the quality and accuracy of material models.

Boerner, Jeremiah James [Sandia National Laborator↗
NASA NTRS1994

The 4-D approach to visual control of autonomous systems

Development of a 4-D approach to dynamic machine vision is described. Core elements of this method are spatio-temporal models oriented towards objects and laws of perspective projection in a foward mode. Integration of multi-sensory measurement data was achieved through spatio-temporal models as invariants for object recognition. Situation assessment and long term predictions were allowed through maintenance of a symbolic 4-D image of processes involving objects. Behavioral capabilities were easily realized by state feedback and feed-foward control.

Dickmanns, Ernst D.↗
NASA NTRS1999

Time-Shifted Boundary Conditions Used for Navier-Stokes Aeroelastic Solver

Under the Advanced Subsonic Technology (AST) Program, an aeroelastic analysis code (TURBO-AE) based on Navier-Stokes equations is currently under development at NASA Lewis Research Center s Machine Dynamics Branch. For a blade row, aeroelastic instability can occur in any of the possible interblade phase angles (IBPA s). Analyzing small IBPA s is very computationally expensive because a large number of blade passages must be simulated. To reduce the computational cost of these analyses, we used time shifted, or phase-lagged, boundary conditions in the TURBO-AE code. These conditions can be used to reduce the computational domain to a single blade passage by requiring the boundary conditions across the passage to be lagged depending on the IBPA being analyzed. The time-shifted boundary conditions currently implemented are based on the direct-store method. This method requires large amounts of data to be stored over a period of the oscillation cycle. On CRAY computers this is not a major problem because solid-state devices can be used for fast input and output to read and write the data onto a disk instead of storing it in core memory.

Srivastava, Rakesh↗
NASA NTRS2006

NASA Tech Briefs, December 2006

Topic include: Inferring Gear Damage from Oil-Debris and Vibration Data; Forecasting of Storm-Surge Floods Using ADCIRC and Optimized DEMs; User Interactive Software for Analysis of Human Physiological Data; Representation of Serendipitous Scientific Data; Automatic Locking of Laser Frequency to an Absorption Peak; Self-Passivating Lithium/Solid Electrolyte/Iodine Cells; Four-Quadrant Analog Multipliers Using G4-FETs; Noise Source for Calibrating a Microwave Polarimeter; Hybrid Deployable Foam Antennas and Reflectors; Coating MCPs with AlN and GaN; Domed, 40-cm-Diameter Ion Optics for an Ion Thruster; Gesture-Controlled Interfaces for Self-Service Machines; Dynamically Alterable Arrays of Polymorphic Data Types; Identifying Trends in Deep Space Network Monitor Data; Predicting Lifetime of a Thermomechanically Loaded Component; Partial Automation of Requirements Tracing; Automated Synthesis of Architecture of Avionic Systems; SSRL Emergency Response Shore Tool; Wholly Aromatic Ether-Imides as n-Type Semiconductors; Carbon-Nanotube-Carpet Heat-Transfer Pads; Pulse-Flow Microencapsulation System; Automated Low-Gravitation Facility Would Make Optical Fibers; Alignment Cube with One Diffractive Face; Graphite Composite Booms with Integral Hinges; Tool for Sampling Permafrost on a Remote Planet; and Special Semaphore Scheme for UHF Spacecraft Communications.

Source record↗
DOE OSTI2024

Atomistic origin of the entropy of melting from inelastic neutron scattering and machine learned molecular dynamics

The latent heat, L, is central to melting, but its atomic origin remains elusive. It is proportional to the entropy of fusion, ΔS fus = L/T m (T m is the melting temperature), which depends on changes of atom configurations, atom vibrations, and thermal electron excitations. Here, we combine inelastic neutron scattering and machine-learned molecular dynamics to separate ΔS fus into these components for Ge, Si, Bi, Sn, Pb, and Li. When the vibrational entropy of melting, ΔS vib , is zero, ΔS fus ≃ 1.2 kB per atom. This result provides a baseline for ΔSconfig and nearly coincides with “Richard’s Rule” of melting. The ΔSfus deviates from this value for most elements, however, and we show that this deviation originates with extra ΔS vib and extra ΔS config . These two components are correlated for positive and negative deviations from Richard’s rule – the extra ΔS config is consistently ~ 80% of ΔS vib . Our results, interpreted with potential energy landscape theory, imply a correlation between the change in the number of basins and the change in the inverse of their curvature for the melting of pure elements.

36 MATERIALS SCIENCE↗
NASA NTRS2020

Machine Learning for Dynamical Modeling of a Flexible Inverted Pendulum System

The inverted pendulum system, a canonical example of an unstable mechanical system, is often used to model the control problems encountered in the flight of rockets in the initial stages of launch, when the airspeed is too small for aerodynamic stability. A system with a flexible pendulum is a variant that more accurately simulates rocket flight nonlinearities (particularly, the flex modes of the rocket). To increase NASA capability of modeling dynamical systems for which closed form solutions are not clear or easily developed, this project aims to provide a machine learning approach that produces a learned dynamical model of the PENNY robot (a rover with a flexible inverted pendulum) from operational data. The developed approach can then be generalized to other complex dynamical systems, including but not limited to rockets and other robotic systems.

M. A. DuPuis↗
DOE OSTI2026

Effects of Composition and Oxidation States on the Structures of Chromium-Containing Sodium Silicate Glasses: Molecular Dynamics Simulations using Machine Learning Interatomic Potentials

Chromium represents a significant challenge for the vitrification of high-level nuclear waste into silicate and borosilicate glasses due to its low solubility and variable oxidation states, which can limit the waste loading due to promotion of crystallization or phase separation during processing. In this study, we modeled chromium containing silicate glasses using molecular dynamics simulations with three machine learning interatomic potentials (MLIPs), MACE, CHGNet, and PFP were employed, to gain insights on glass composition and oxidation states on the structures of these glasses. One of the goals is to evaluate their ability of these MLIPs to accurately represent the general structure of silicate glasses and chromium local environments as a function of chromium oxidation states. Density Functional Theory (DFT) based calculations and experimental data such as neutron structure factors were used to validate the structural models. It was found that the foundation models of all three MLIPs are able to reproduce general structural features of the sodium silicate glass structure consistent with experimental and DFT data, but only CHGNet and PFP can accurately capture the oxidation states and local environment of chromium: tetrahedral for Cr6+ and octahedral for Cr3+. Furthermore, we studied the effect of varying Cr3+/ Cr6+ (Cr3+/Crtotal) ratio and total chromium content using PFP. Our results show that Cr6+ enhances network polymerization by reducing non-bridging oxygens through Na? charge compensation required due to the formation of chromate (CrO42-) species, while Cr³? acts as a network modifier that disrupts connectivity. System size effects on the structural characteristics and chromium environments were also tested using the PFP potential. This work highlights the importance of careful validation on the precision, transferability, and potential of MLIPs for modeling glasses containing transition metal elements that can exist in multiple oxidation states. It is also encouraging to see the foundational models are all three MLFFs are able to reproduce the basic sodium silicate glass structures, while suggesting additional training or refining is needed to improve the description of more complex systems containing transition metals.

Puga, Christina L.↗
DOE OSTI2024

Iron Impurity Impairs the CO 2 Capture Performance of MgO: Insights from Microscopy and Machine Learning Molecular Dynamics

Magnesium oxide (MgO) is a promising sorbent for direct air capture (DAC) of carbon dioxide. Iron (Fe) is a common impurity in naturally occurring MgO and minerals used to produce MgO, yet a molecular-scale understanding of Fe-doping effects on carbonation is lacking. Here, in this study, we observed reduced carbonation performance in Fe-doped MgO experimentally. The energetics of adsorbing a (bi)carbonate ion on pristine and Fe-doped MgO(001) surfaces were further investigated using ab initio and machine learning potential molecular dynamics coupled with metadynamics simulations. Both pristine and Fe-doped surfaces exhibited a basic (OH – ) hydration layer, where the (bi)carbonate ion adsorption is thermodynamically favorable. However, the dissolution of surface Fe had smaller energy barriers and was more favorable than Mg. Leached Fe likely neutralized the near-surface basicity, yielding reduced reactivity on Fe-doped MgO. Our observations offer critical insights for material selection and emphasize the importance of evaluating the geologic origin of earth materials used for DAC.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Elucidating Lithium Transport Mechanisms in Disordered LiF from Machine-Learning Molecular Dynamics Simulations

Lithium fluoride (LiF) is a ubiquitous component of solid- and cathode–electrolyte interphases, yet its functional role remains unclear under the structural and chemical heterogeneity typical of cycling batteries. Here, we systematically quantify how structural disorder, off-stoichiometry, and strain govern Li-ion transport in LiF. Using a machine-learning potential to enable extensive molecular-dynamics sampling, we compare crystalline and amorphous LiF, Li 0.95 F, and LiF 0.95 , and evaluate the impact of small homogeneous deformations. Defect-free crystalline LiF is effectively ion-blocking at 300−500 K, whereas amorphization generates free-volume–assisted percolation pathways that facilitates Li-ion diffusion. At elevated temperatures, thermodynamically driven crystallization disrupts these pathways, leading to non-Arrhenius behavior. In crystalline phases, Li deficiency activates vacancy-mediated diffusion, while in amorphous LiF, transport is governed primarily by network connectivity. Strain is found to have only a marginal effect on Li mobility in both crystalline and amorphous structures.

Batteries↗
DOE OSTI2026

How Silica Surface Chemistry Modulates Interfacial Water: Insights from Machine Learning Molecular Dynamics

Controlling water structure and dynamics at silica interfaces are central to a wide range of technologies, including protective oxide layers for solar water splitting and nanoporous membranes. In this work, we develop a machine learning interatomic potential, trained via active learning, to achieve ab initio accuracy for water confined between hydroxylated silica surfaces over a range of silanol coverages and slit widths. We find that partially hydroxylated surfaces (50 and 75% OH) support stronger water−surface hydrogen bonding and more extended interfacial density profiles than fully hydroxylated (100% OH) surfaces, indicating that increasing OH coverage does not necessarily strengthen interfacial hydrogenbond networks. Translational diffusion decreases approximately linearly with slit width and OH coverage, whereas rotational dynamics respond nonlinearly. In particular, at the smallest slit width of 5 Å, 75% OH coverage produces an enhanced local tetrahedral ordered interfacial network that strongly suppresses reorientation, while 100% coverage yields a crowded, disordered interfacial layer that also hinders rotation. In contrast, the 50% OH coverage is sufficiently sparse that it does not markedly alter water structure or dynamics under confinement. These results show that coupled control of pore size and surface chemistry enables nonlinear tuning of interfacial water structure and transport, providing a design strategy for optimizing porous silica for either enhanced interfacial stability and controlled reactivity or rapid and selective transport.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRSDate not supplied

Machine Learning for Dynamic Test Sensor Placement

There are multiple different algorithms to perform modal test sensor placement optimization: effective independence, residual kinetic energy, iterative Guyan reduction, genetic algorithms, or a brute-force methodology. However, any of these methods may be computationally expensive, especially for structural models with a large number of degrees of freedom. Given the high-cost and the need to optimize the solution, modal sensor placement is a great application for machine learning (ML) algorithms. In this paper, we will apply ML algorithms to determine the optimal sensor locations for simple and complex structures. We will also discuss the benefits and drawbacks of using machine learning over other sensor placement algorithms.

Kelsey Buckles↗
DOE OSTI2024

Effects of Nonequilibrium Atomic Structure on Ionic Diffusivity in LLZO: A Classical and Machine Learning Molecular Dynamics Study

To improve the performance of electrochemical devices, it is essential to understand the effects of nonequilibrium motifs in solids, such as grain boundaries, amorphous phases, and highly strained regions, on atomic-scale transport and stability. Molecular dynamics simulations are used to explore the combined effect of far-from-equilibrium atomic structures and the choice of interatomic potential on ionic diffusivity predictions for Li 7 La 3 Zr 2 O 12 (LLZO), a promising solid electrolyte for all-solid-state batteries. Amorphization and high strain are considered using both classical Buckingham interatomic potentials and machine learning force fields. Here we find that both crystalline expansion and amorphization tend to slow diffusion, although the different physical encodings in the two potentials impact the properties in different ways. We trace these variations to a combination of structural and transport factors, the contributions of which are deconvoluted computationally. Graph-based analysis reveals that the variations for amorphous LLZO arise from the connectivity of diffusion pathways within the predicted structures, which generally correlates with diffusivity and is notably higher for structures generated by the machine learning force fields. Our study provides additional insight into the relationship between atomic structure and diffusivity in LLZO, while also highlighting the need for care in choosing and validating potentials to simulate far from equilibrium structures.

25 ENERGY STORAGE↗
NASA NTRS2019

Using Machine-Learning to Dynamically Generate Operationally Acceptable Strategic Reroute Options

The newly developed Trajectory Option Set (TOS), a preference-weighted set of alternative routes submitted by flight operators, is a capability in the U.S. traffic flow management system that enables automated trajectory negotiation between flight operators and Air Navigation Service Providers. The objective of this paper is to describe and demonstrate an approach for automatically generating pre-departure and airborne TOSs that have a high probability of operational acceptance. The approach uses hierarchical clustering of historical route data to identify route candidates. The probability of operational acceptance is then estimated using predictors trained on historical flight plan amendment data using supervised machine learning algorithms, allowing the routes with highest probability of operational acceptance to be selected for the TOS. Features used describe historical route usage, difference in flight time and downstream demand to capacity imbalance. A random forest was found to be the best performing algorithm for learning operational acceptability, with a model accuracy of 0.96. The approach is demonstrated for an historical pre-departure flight from Dallas/Fort Worth International Airport to Newark Liberty International Airport.

Evans, Antony↗
NASA NTRS2019

Using Machine-Learning to Dynamically Generate Operationally Acceptable Strategic Reroute Options

The newly developed Trajectory Option Set (TOS), a preference-weighted set of alternative routes submitted by flight operators, is a capability in the U.S. traffic flow management system that enables automated trajectory negotiation between flight operators and Air Navigation Service Providers. The objective of this paper is to describe and demonstrate an approach for automatically generating pre-departure and airborne TOSs that have a high probability of operational acceptance. The approach uses hierarchical clustering of historical route data to identify route candidates. The probability of operational acceptance is then estimated using predictors trained on historical flight plan amendment data using supervised machine learning algorithms, allowing the routes with highest probability of operational acceptance to be selected for the TOS. Features used describe historical route usage, difference in flight time and downstream demand to capacity imbalance. A random forest was found to be the best performing algorithm for learning operational acceptability, with a model accuracy of 0.96. The approach is demonstrated for an historical pre-departure flight from Dallas/Fort Worth International Airport to Newark Liberty International Airport.

Evans, Antony↗