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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Machine Learning–Guided Boolean Matrix Inference for Real-Time O-RAN Conflict Detection

Open Radio Access Networks (O-RAN) are emerging, software-driven cellular architectures that promote flexibility by enabling components from different vendors to interoperate. Multiple control applications called xApps can independently adjust network parameters in near real time, often without awareness of each other's actions. This creates a system highly prone to unintended conflicts and performance degradation due to the inherent complexity of such openness. To model such systems and ultimately prevent or mitigate xApp conflicts, it is essential to understand the dynamic relationships between xApps (A), the control parameters they adjust (P), and the resulting KPI responses (K). While the mappings from A to P and from K to A can often be derived from xApp specifications, the relationship from P to K is typically hidden within the system’s dynamics and must be inferred from observed data. We propose a novel data-driven Boolean inference framework that uncovers the hidden P?K dependencies using machine learning and interpretable rule induction. Continuous parameters and KPIs are first binarized using decision tree classifiers, and a binary influence matrix L is then inferred by solving Boolean matrix equations over time. This compact representation improves interpretability and enables real-time tracking of dynamically evolving parameter-KPI dependencies. We demonstrate the effectiveness of our method in a realistic mobile handover scenario, where it accurately recovers the underlying logic and enables proactive conflict detection.

42 - ENGINEERING↗
DOE OSTI2025

First-Principles Assessment of ZnTe and CdSe as Prospective Tunnel Barriers at the InAs/Al Interface

Majorana zero modes are predicted to emerge in semiconductor/ superconductor interfaces, such as InAs/Al. Majorana modes could be utilized for fault tolerant topological qubits. However, their realization is hindered by materials challenges. The coupling between the superconductor and the semiconductor may be too strong for Majorana modes to emerge, due to effective doping of the semiconductor by the metallic contact. This could be mediated by adding a tunnel barrier of controlled thickness. We use density functional theory (DFT) with Hubbard U corrections, whose values are machine-learned via Bayesian optimization (BO), to assess ZnTe and CdSe as prospective tunnel barriers for the InAs/Al interface. The results of DFT +U(BO) for ZnTe are validated by comparison to angle resolved photoemission spectroscopy (ARPES). We then study bilayer interfaces of the three semiconductors with each other and with Al, as well as trilayer interfaces with a varying number of ZnTe or CdSe layers inserted between InAs and Al. We find that 16 atomic layers of either material completely insulate the InAs from metal induced gap states (MIGS). However, ZnTe and CdSe differ significantly in their band alignment, such that ZnTe forms an effective barrier for electrons, whereas CdSe forms a barrier for holes. Because of Fermi level pinning in the conduction band at the interface, only electron transport is relevant for InAs-based Majorana devices. Therefore, ZnTe is the better choice. Based on the results of our simulations, we suggest conducting experiments with ZnTe barriers in the thickness range of 6–18 atomic layers.

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

Highest fusion performance without harmful edge energy bursts in tokamak

The path of tokamak fusion and International thermonuclear experimental reactor (ITER) is maintaining high-performance plasma to produce sufficient fusion power. This effort is hindered by the transient energy burst arising from the instabilities at the boundary of plasmas. Conventional 3D magnetic perturbations used to suppress these instabilities often degrade fusion performance and increase the risk of other instabilities. This study presents an innovative 3D field optimization approach that leverages machine learning and real-time adaptability to overcome these challenges. Implemented in the DIII-D and KSTAR tokamaks, this method has consistently achieved reactor-relevant core confinement and the highest fusion performance without triggering damaging bursts. This is enabled by advances in the physics understanding of self-organized transport in the plasma edge and machine learning techniques to optimize the 3D field spectrum. The success of automated, real-time adaptive control of such complex systems paves the way for maximizing fusion efficiency in ITER and beyond while minimizing damage to device components.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Integrated design of aluminum-enriched high-entropy refractory B2 alloys with synergy of high strength and ductility

Refractory high-entropy alloys (RHEAs) are promising high-temperature structural materials. Their large compositional space poses great design challenges for phase control and high strength-ductility synergy. The present research pioneers using integrated high-throughput machine learning with Monte Carlo simulations supplemented by ab initio calculations to effectively navigate phase selection and mechanical property predictions, developing single-phase ordered B2 aluminum-enriched RHEAs (Al-RHEAs) demonstrating high strength and ductility. These Al-RHEAs achieve remarkable mechanical properties, including compressive yield strengths up to 1.7 gigapascals, fracture strains exceeding 50%, and notable high-temperature strength retention. They also demonstrate a tensile yield strength of 1.0 gigapascals with a ductility of 9%, albeit with B2 ordering. Furthermore, we identify valence electron count domains for alloy ductility and brittleness with the explanation from density functional theory and provide crucial insights into elemental influence on atomic ordering and mechanical performance. The work sets forth a strategic blueprint for high-throughput alloy design and reveals fundamental principles governing the mechanical properties of advanced structural alloys.

Science & Technology - Other Topics↗
DOE OSTI2026

Recent advances in plasma control and physics research in the Large Helical Device

The Large Helical Device (LHD), the largest superconducting helical system in the world, is equipped with advanced heating and diagnostic tools, facilitating plasma control and physics research. Data assimilation was employed for electron temperature control using a real-time Thomson scattering system and real time prediction code. A virtual LHD environment enabled visualization of escaping high-energy tritium ions and demonstrated that these ions impact the rear side of the divertor plate. Pioneering results crucial to plasma control have also been achieved. Real-time wall conditioning using Lithium granule dropping improved bulk ion energy and particle transport while simultaneously enhancing the heavy impurity transport. Progress has also been made in the investigation of turbulence-driven transport. At the confinement bifurcation, ion-scale turbulence decreased, while electron-scale turbulence increased. A change in the anisotropy of turbulent eddies was also observed at the confinement bifurcation. Coexistence of local and non-local turbulence was identified in electron-scale turbulence. Non-local turbulence exhibited the rapid spatial propagation of perturbations throughout the plasma, while local turbulence followed the temperature gradient. A transition between drift-wave turbulence and magnetohydrodynamics (MHD) turbulence was observed with the turbulence minimized at the transition condition. Machine learning analysis was employed to evaluate the temperate and density conditions of this turbulence transition. Then, real-time control of fueling and heating was applied to maintain the turbulence transition condition, improving the energy confinement enhancement factor by 20%. In addition, evidence was obtained for collisionless ion heating by energetic-ion-driven geodesic acoustic modes and MHD bursts. These achievements represent unique contributions to the development of fusion reactors.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

From atomistic models to machine learning: Predictive design of nanocarbons under extreme conditions

The formation of technologically valuable nanocarbon structures under extreme conditions, such as those produced during high-explosive detonations, remains poorly understood but holds significant potential for the development of controlled synthesis pathways. While detonation shockwaves provide the high-pressure, high-temperature environment required for nanodiamond formation, subsequent cooling and decompression dictate whether the diamond phase is preserved or transformed into other nanocarbon structures. Here, in this study, we employ GPU-accelerated reactive molecular dynamics (ReaxFF) simulations to investigate the graphitization and structural remodeling of detonation nanodiamond under nonlinear quench and pressure-release trajectories. We further investigate how the initial nanodiamond morphology; cuboctahedral, octahedral, or hexagonal prism influences the resulting transformation products. Evolution of nanostructure, allotrope (via simulated x-ray diffraction), carbon hybridization, and ring statistics are tracked during a two-stage quench from 5000 K to 60 GPa. Rapid cooling combined with slow decompression optimizes cubic diamond retention, whereas slow cooling with rapid pressure release promotes surface-to-core graphitization, producing concentric sp 2 -hybridized layers and hollowed inner shells. Octahedral nanodiamonds evolve into carbon nano-onions, initially forming bucky diamonds that progressively transform into fully sp 2 -hybridized structures, while hexagonal prisms preferentially form parallel-stacked graphite layers resembling carbon dots. Transient hexagonal diamond (lonsdaleite) emerges as an interfacial phase, suggesting potential reversibility in the shock-induced graphite-to-diamond transformation pathway transformation route. To extend predictive capabilities, we trained machine learning (ML) regressors on over 10 5 node-hours of molecular dynamics (MD) trajectories. A multilayer perceptron (MLP) model reliably predicts the number of graphitized layers from temperature–pressure trajectories with a coefficient of determination (R 2 ) exceeding 0.90. This high predictive fidelity enables efficient, high-throughput mapping of the synthesis parameter space for optimized graphitization outcomes. Collectively, morphological control combined with optimized quench–decompression conditions promote the selective synthesis of nanocarbon allotropes. This work establishes a data-driven framework for the rational, a priori design of carbon nanomaterials for applications in energy storage, sensing, and biomedicine.

Detonation nanodiamond remodeling↗
DOE OSTI2024

Simulation-trained machine learning models for Lorentz transmission electron microscopy

Understanding the collective behavior of complex spin textures, such as lattices of magnetic skyrmions, is of fundamental importance for exploring and controlling the emergent ordering of these spin textures and inducing phase transitions. It is also critical to understand the skyrmion–skyrmion interactions for applications such as magnetic skyrmion-enabled reservoir or neuromorphic computing. Magnetic skyrmion lattices can be studied using in situ Lorentz transmission electron microscopy (LTEM), but quantitative and statistically robust analysis of the skyrmion lattices from LTEM images can be difficult. In this work, we show that a convolutional neural network, trained on simulated data, can be applied to perform segmentation of spin textures and to extract quantitative data, such as spin texture size and location, from experimental LTEM images, which cannot be obtained manually. This includes quantitative information about skyrmion size, position, and shape, which can, in turn, be used to calculate skyrmion–skyrmion interactions and lattice ordering. We apply this approach to segmenting images of Néel skyrmion lattices so that we can accurately identify skyrmion size and deformation in both dense and sparse lattices. The model is trained using a large set of micromagnetic simulations as well as simulated LTEM images. This entirely open-source training pipeline can be applied to a wide variety of magnetic features and materials, enabling large-scale statistical studies of spin textures using LTEM.

McCray, Arthur R. C. (ORCID:0000000160774698)↗
DOE OSTI2026

Stoichiometry dependent properties of cerium hydride: An active learning developed interatomic potential study

Cerium hydride has a variety of interesting properties, including a known lattice contraction and densification with increasing hydrogen content. However, precise stoichiometric control is not experimentally straightforward and ab initio approaches are not computationally feasible for many properties such as melting and low temperature diffusion. Therefore, we develop a machine-learned interatomic potential for cerium hydride that is valid for H to Ce ratios from 2.0 to 3.0. A query-by-committee active learning approach is used to develop the training set. Leveraging classical molecular dynamics simulations, we assess a range of properties and provide fundamental mechanisms for the trends with stoichiometry. Finally, a majority of the properties follow the trend of lattice contraction, being governed by the stronger lattice binding induced by adding octahedral atoms.

36 MATERIALS SCIENCE↗
NASA NTRS1993

Representation in incremental learning

Work focused on two areas in machine learning: representation for inductive learning and how to apply concept learning techniques to learning state preferences, which can represent search control knowledge for problem solving. Specifically, in the first area the issues of the effect of representation on learning, on how learning formalisms are biased, and how concept learning can benefit from the use of a hybrid formalism are addressed. In the second area, the issues of developing an agent to learn search control knowledge from the relative values of states, of the source of that qualitative information, and of the ability to use both quantitative and qualitative information in order to develop an effective problem-solving policy are examined.

Source record↗
NASA NTRS2022

Natural Language Understanding and Extraction of Flight Constraints Recorded in Letters of Agreement

This paper presents an automated information extraction and inference technique using natural language processing for extracting flight operational procedures and constraints embedded in heritage air traffic management documents. The extracted flight constraints can be digitized and fit into existing airspace information exchange models such as the Aeronautical Information Exchange Model (AIXM). This approach offers a digitized solution to disseminate airspace operating conditions to diverse air users and stakeholders in the National Airspace System (NAS). Furthermore, the digitized flight procedures can provide operational flexibility for emerging advanced air mobility providers and reduce traffic controller workload while maintaining current safety standards. To demonstrate this process, 1,972 Letters of Agreement (LOAs) have been selected for processing, named entity extraction, constraint identification and extraction. This dataset is derived from a subset of documents related to Air Route Traffic Control Centers (ARTCC) operations. We experimented with various traditional information extraction techniques, state-of-the-art machine learning and deep learning models to perform named entity recognition and pattern recognition on our dataset. We present the results from our experiments and demonstrate 99.0% F-1 score for named entity recognition, and a 96.6% accuracy for our entire workflow up to named entity recognition. We also discuss constraint definitions using generic patterned templates and extensions to this work in applying entity linking to digitally extracting relevant constraints.

Natural Language Processing↗
NASA NTRSDate not supplied

Natural Language Understanding and Extraction of Flight Constraints Recorded in Letters of Agreement

This paper presents an automated information extraction and inference technique using natural language processing for extracting flight operational procedures and constraints embedded in heritage air traffic management documents. The extracted flight constraints can be digitized and fit into existing airspace information exchange models such as the Aeronautical Information Exchange Model (AIXM). This approach offers a digitized solution to disseminate airspace operating conditions to diverse air users and stakeholders in the National Airspace System (NAS). Furthermore, the digitized flight procedures can provide operational flexibility for emerging advanced air mobility providers and reduce traffic controller workload while maintaining current safety standards. To demonstrate this process, 1,972 Letters of Agreement (LOAs) have been selected for processing, named entity extraction, constraint identification and extraction. This dataset is derived from a subset of documents related to Air Route Traffic Control Centers (ARTCC) operations. We experimented with various traditional information extraction techniques, state-of-the-art machine learning and deep learning models to perform named entity recognition and pattern recognition on our dataset. We present the results from our experiments and demonstrate 99.0% F-1 score for named entity recognition, and a 96.6% accuracy for our entire workflow up to named entity recognition. We also discuss constraint definitions using generic patterned templates and extensions to this work in applying entity linking to digitally extracting relevant constraints.

Natural Language Processing↗
DOE OSTI2024

Machine Learning of Plasma Science for Next Generation Microelectronics (Project Final Report)

Low temperature plasmas (LTPs) are an enabling technology behind reducing device dimensions and the continuation of Moore’s Law. It is estimated that 40-45% of all process steps necessary to manufacture semiconductor devices involve LTPs. However, challenges in plasma process design and continuous incorporation of novel materials for new device architectures are pushing the limits of what is possible with current plasma technology. For example, creating higher aspect ratio structures and etching features at the atomic scale both require finer control of the ion energy/velocity at wafer surfaces. To support these types of future innovations in the plasma processing systems that Sandia and the DOE rely upon, we have developed novel diagnostics, simulations, and machine learning capabilities to discover, characterize, and predict plasma phenomena affecting the ion energy/velocity distribution function (IEDF). These efforts also supported research program development and external collaboration with industry and academia through Sandia’s Plasma Research Facility (PRF). This report will focus on the following topics and accomplishments of this three year LDRD project, briefly summarized.

42 ENGINEERING↗
DOE OSTI2025

Transferable dispersion-aware machine learning interatomic potentials for multilayer transition metal dichalcogenide heterostructures

Stacking atomically thin transition metal dichalcogenides (TMDs) into heterostructures enables exploration of exotic quantum phases, particularly through twist-angle-controlled moiré superlattices. These structures exhibit novel electronic and optical behaviors driven by atomic-scale structural reconstruction. However, studying such systems with DFT is computationally demanding due to their large unit cells and van der Waals (vdW) interactions between layers. To address this, we develop a transferable neural network potential (NNP) that includes long-range vdW corrections up to 12Å with minimal overhead. Trained on vdW-corrected DFT data for Mo- and W-based TMDs with S, Se, and Te, the NNP accurately models monolayers, bilayers, heterostructures, and their interaction with h-BN substrates. It reproduces equilibrium structures, energy landscapes, phonon dispersions, and matches experimental atomic reconstructions in twisted WS2 and MoS2/WSe2 systems. We demonstrate that our NNP achieves DFT-level accuracy and high computational efficiency, enabling large-scale simulations of TMD-based moiré superlattices both with and without substrates.

materials for energy and catalysis↗
NASA NTRS2016

Taxi Time Prediction at Charlotte Airport Using Fast-Time Simulation and Machine Learning Techniques

Accurate taxi time prediction is required for enabling efficient runway scheduling that can increase runway throughput and reduce taxi times and fuel consumptions on the airport surface. Currently NASA and American Airlines are jointly developing a decision-support tool called Spot and Runway Departure Advisor (SARDA) that assists airport ramp controllers to make gate pushback decisions and improve the overall efficiency of airport surface traffic. In this presentation, we propose to use Linear Optimized Sequencing (LINOS), a discrete-event fast-time simulation tool, to predict taxi times and provide the estimates to the runway scheduler in real-time airport operations. To assess its prediction accuracy, we also introduce a data-driven analytical method using machine learning techniques. These two taxi time prediction methods are evaluated with actual taxi time data obtained from the SARDA human-in-the-loop (HITL) simulation for Charlotte Douglas International Airport (CLT) using various performance measurement metrics. Based on the taxi time prediction results, we also discuss how the prediction accuracy can be affected by the operational complexity at this airport and how we can improve the fast time simulation model before implementing it with an airport scheduling algorithm in a real-time environment.

airport surface traffic↗
DOE OSTI2026

Feedforward-feedback ammonia control at a water resource recovery facility based on a digital twin with hybrid model

Ammonia-based aeration control (ABAC) at full-scale Water Resource Recovery Facilities (WRRFs) can be challenged by diurnal loading and transport delays. This work addressed these challenges using a hybrid feedforward–feedback controller built on Activated Sludge Model 1 (ASM1), marking the first full-scale deployment to pair a mechanistic feedforward core with data-driven corrections. The objectives were to improve ammonia setpoint tracking, assess performance of the mechanistic model when enhanced with data-driven corrections, and document full-scale operation. The hybrid model incorporates two data-driven components: (1) a Mechanistic Error Forecasting Engine (MEFE), consisting of a multivariate linear regressor and a long short-term memory (LSTM) ensemble. Defying expectations, low-parameter models outperformed more complex alternatives, reducing the mechanistic error by 71%. (2) A Residual Oscillation Forecasting Engine (ROFE), based on Fast Fourier Transform, reduced the remaining error by another 35%. Two proportional–integral (PI) feedback loops further (i) trim the feedforward output and (ii) eliminate residual controller error in the final aerobic zone. In full-scale operation, the controller reduced mean-squared error (MSE) by 94% over the baseline and produced more stable dissolved oxygen (DO) setpoints. Overall, it was proven that layering multi-timescale data-driven models on a mechanistic core can yield reliable ABAC performance at WRRFs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Hybrid renewable energy systems

In the pursuit of ecologically sustainable and resilient energy systems, increasingly more attention is being devoted to a diversity of energy generation and storage methods. As the landscape of generation technology gains nuance and complexity, a wide-ranging set of technical questions has emerged, touching on topics that range from control and optimization of hybrid systems to finance and economic viability to multi-fidelity modeling and scientific machine learning. In the context of this special issue, hybrid renewable energy systems are any systems that consider the combined dynamics of more than one form of generation, storage, or grid subsystem. Research endeavors have delved into improving the flexibility of energy systems by utilizing existing resources, introducing novel operational strategies, deploying enhanced renewable forecasts, and exploring emerging technologies. In conclusion, the interconnection among various sectors has garnered heightened attention, not only due to the provision of additional tradable energy products but also for furnishing flexible headroom to system operators.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Evidence for B + → K + ν ν ¯ decays

We search for the rare decay B + → K + ν ν ¯ in a 362 fb − 1 sample of electron-positron collisions at the ϒ ( 4 S ) resonance collected with the Belle II detector at the SuperKEKB collider. We use the inclusive properties of the accompanying B meson in ϒ ( 4 S ) → B B ¯ events to suppress background from other decays of the signal B candidate and light-quark pair production. We validate the measurement with an auxiliary analysis based on a conventional hadronic reconstruction of the accompanying B meson. For background suppression, we exploit distinct signal features using machine learning methods tuned with simulated data. The signal-reconstruction efficiency and background suppression are validated through various control channels. The branching fraction is extracted in a maximum likelihood fit. Our inclusive and hadronic analyses yield consistent results for the B + → K + ν ν ¯ branching fraction of [ 2.7 ± 0.5 ( stat ) ± 0.5 ( syst ) ] × 10 − 5 and [ 1.1 − 0.8 + 0.9 ( stat ) − 0.5 + 0.8 ( syst ) ] × 10 − 5 , respectively. Combining the results, we determine the branching fraction of the decay B + → K + ν ν ¯ to be [ 2.3 ± 0.5 ( stat ) − 0.4 + 0.5 ( syst ) ] × 10 − 5 , providing the first evidence for this decay at 3.5 standard deviations. The combined result is 2.7 standard deviations above the standard model expectation. Published by the American Physical Society 2024

Astronomy & Astrophysics↗