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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Renewable Energy and Storage Cybersecurity Research (RESCue) Pilot Final Report

The Renewable Energy and Storage Cybersecurity Research (RESCue) project is a collaborative effort aimed at securing the rapidly growing deployment of distributed energy resources (DERs) and transmission-connected hybrid renewable energy systems against escalating cyber threats. This project brings together major original equipment manufacturers (OEMs) of wind, solar, and energy storage, along with major asset owners and DOE National Laboratories, to collectively identify cyber threats, assess risks, and develop robust cybersecurity strategies and solutions. This publication the final report for the first year of the project.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Data-Driven Recommendation of Optimal Tuning Scheme for Range-Separated Hybrid Functionals in Solution-Phase UV/Vis Absorption Energy Prediction

Time-dependent density functional theory (TDDFT) combined with range-separated hybrid (RSH) functionals and a tuned range-separation parameter γ offers a computationally economical approach for high-throughput excited- state property predictions. The γ-tuning procedure in the gas phase is well established. However, no agreement on the best γ- tuning procedure has been made when considering the solvent effect with implicit solvent models like the polarizable continuum model (PCM). To answer that question, this study created a diverse dataset with 937 molecules with experimental solutionphase UV/vis absorption spectra. Three γ-tuning methods, the gasphase γ-tuning (GPγT), the partial vertical γ-tuning (PVγT), and the strict vertical γ-tuning (SVγT), were evaluated for the ωPBEh functional over the entire dataset. Additional benchmarks are done for the optimally tuned screened range-separated hybrid combined with the PCM approach (SRSH-PCM) and the solvation-mediated tuning procedure (sol-med-OT). Our findings revealed that the optimal γ-values obtained by the PVγT and the SVγT are significantly smaller than the GPγT. This trend holds consistently across all molecules in our dataset, and we explained the origin of this phenomenon. TDDFT calculations with PVγTand SVγT-tuned γ-values and default global Fock exchange fraction achieve superior performance compared to those using GPγTtuned or default γ and slightly outperform SRSH-PCM and sol-med-OT with similar or lesser computational cost. Furthermore, we found that the smaller γ-values from SVγT captured the expected 1/(εR) asymptotic behavior in the solution phase, resulting in accurate prediction of solution-phase CT excitations, consistent with the screened asymptote behavior encoded in SRSH-PCM. These results show that SVγT is the best scheme for high-throughput UV/vis absorption spectrum calculations using the ωPBEh functional from a data-driven perspective.

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

On optimal control of hybrid dynamical systems using complementarity constraints

Optimal control for switch-based dynamical systems is a challenging problem in the process control literature. In this study, we model these systems as hybrid dynamical systems with finite number of unknown switching points and reformulate them using non-smooth and non-convex complementarity constraints as a mathematical program with complementarity constraints (MPCC). We utilize a moving finite element based strategy to discretize the differential equation system to accurately locate the unknown switching points at the finite element boundary and achieve high-order accuracy at intermediate non-collocation points. We propose a globalization approach to solve the discretized MPCC problem using a mixed NLP/MILP-based strategy to converge to a non-spurious first-order optimal solution. The method is tested on three dynamic optimization examples, including a gas–liquid tank model and an optimal control problem with a sliding mode solution.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Regularizing INR with Diffusion Prior for Self-Supervised 3D Reconstruction OF Neutron Computed Tomography Data

Recently, generative diffusion priors have made huge strides as inverse problem solvers, including the ability to be adapted for inference on out-of-distribution data. Concurrently, implicit neural representations (INRs) have emerged as fast and lightweight inverse imaging solvers that are amenable to hybrid approaches that combine learned priors with traditional inverse problem formulations. In this paper, we present a diffusive computed tomography (CT) inversion framework for regularizing INRs called Diffusive INR (DINR), designed to enable high-quality reconstruction from sparse-view neutron CT. Pretrained purely on synthetic data, DINR is evaluated on simulated and experimentally obtained observations of concrete microstructures, where traditional reconstruction methods suffer substantial degradation when the number of views is reduced. Our approach delivers superior performance, reduces reconstruction artifacts, and achieves gains in PSNR and SSIM, enabling accurate micro-structural characterization even under extreme data limitations compared to state-of-the-art sparse-view reconstruction techniques.

Hossain, Maliha [ORNL]↗
DOE OSTI2025

A deep learning and finite element approach for exploration of inverse structure–property designs of lightweight hybrid composites

Hybrid composites have important applications, such as high-performance and lightweight materials in aerospace and automotive industries. Hybrid composites utilize the synergy of diverse fillers to achieve desired material properties, but usually have more complicated microstructures. While topology optimization can optimize a particular property, designing hybrid composites for customized mechanical performances, e.g. full-range stress–strain curve, remains challenging. Here, a computational framework that integrated finite element analysis (FEA) and artificial intelligence (AI) methods of Conditional Generative Adversarial Networks (cGAN) deep learning and transfer learning was developed to establish inverse structure–property relationships and design tailor-made hybrid composites. Based on FEA-generated datasets of hybrid fiber-particle–matrix microstructures and their corresponding full-range stress–strain curves, a cGAN architecture was trained to generate tailored microstructures and establish structure–property relationships. Similarity in microstructural features and well-matched stress–strain curves based on the AI-generated composites were achieved. In conclusion, transfer learning was used to expand the pre-trained model for designing different materials systems.

Hybrid composites↗
DOE OSTI2026

Modeling Framework for Data Center

This chapter highlights the critical need for advanced modeling of data centers due to their rapidly increasing energy consumption and impact on grid reliability. Driven by the demand for AI applications, data centers are projected to consume a significant portion of US energy by 2028, putting stress on an already challenged power grid. The chapter emphasizes the importance of "fast" time-scale models to understand the dynamic interactions between data centers and the grid, especially given the rapid power fluctuations of AI workloads. It outlines a modeling framework that includes both offline and real-time EMT domain simulations, detailing the necessary representations for various components like utility interfaces, transformers, IT loads, UPS, cooling loads, Battery Energy Storage Systems (BESS), generators, protection systems, and higher-level control systems. While standard simulation tools like PSCAD offer basic models, custom development is often required to accurately capture the unique and fast-changing behaviors of modern data centers. The chapter also discusses key metrics and test cases for validating these models, focusing on transient load responses, protection relay coordination, and demand flexibility. Finally, it addresses the challenges of modeling large-scale data centers, such as computational complexity and the trade-off between model fidelity and practicality, suggesting hybrid modeling approaches as a solution. The overarching goal is to create a robust framework that helps assess data center impacts on grid stability, identify vulnerabilities, and inform the development of standards for reliable integration of these large loads into the bulk power system.

25 ENERGY STORAGE↗
DOE OSTI2024

Runtime performance of a GAMESS quantum chemistry application offloaded to GPUs

Summary Computational chemistry is at the forefront of solving urgent societal problems, such as polymer upcycling and carbon capture. The complexity of modeling these processes at appropriate length and time scales is mainly manifested in the number and types of chemical species involved in the reactions and may require models of several thousand atoms and large basis sets to accurately capture the chemical complexity and heterogeneity in the physical and chemical processes. The quantum chemistry package General Atomic and Molecular Electronic Structure System (GAMESS) has a wide array of methods that can efficiently and accurately treat complex chemical systems. In this work, we have used the GAMESS Effective Fragment Molecule Orbital (EFMO) method for electronic structure calculation of a challenging mesoporous silica nanoparticle (MSN) model surrounded by about 4700 water molecules to investigate the strong scaling and GPU offloading on hybrid CPU‐GPU nodes. Experiments were performed on the Perlmutter platform at the National Energy Research Scientific Computing Center. Good strong scaling and load balancing have been observed on up to 88 hybrid nodes for different settings of the execution parameters for the calculation considered here. When GPUs are oversubscribed by offloading work from multiple CPU processes, using the NVIDIA multi‐process service (MPS) has consistently reduced time to solution and energy consumed. Additionally, for some configuration parameter settings, oversubscription with MPS improved performance by up to 5.8% over the case without oversubscription.

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

Systematic input scheme for many-boson Hamiltonians with applications to the two-dimensional 𝜙 4 theory

We develop a novel, systematic input scheme for many-boson Hamiltonians in order to solve field theory problems within the light-front Hamiltonian formalism via quantum computing. We present our discussion of this input scheme based on the light-front Hamiltonian of the two-dimensional ϕ 4 theory. In our input scheme, we employ a set of quantum registers, where each register encodes the occupation of a distinct boson mode as binaries. We squeeze the boson operators of each mode and present the Hamiltonian in terms of unique combinations of the squeezed-boson operators. We design the circuit modules for these unique combinations. Based on these circuit modules, we block encode the many-boson Hamiltonian utilizing the idea of quantum walk. For demonstration purposes, we present the spectral calculations of the Hamiltonian utilizing the hybrid quantum-classical symmetry-adapted quantum Krylov subspace diagonalization algorithm based on our input scheme, where the quantum computations are performed with the IBM Qiskit quantum simulator. The results of the hybrid calculations agree with exact results. Here, we can incorporate the input scheme in this work with the input scheme for many-fermion Hamiltonians; they jointly offer new pathways to solving the structure and dynamics of more general field theory problems on future fault-tolerant quantum computers.

Ab initio calculations↗
DOE OSTI2026

ALD with Alternative Coreactants: Which Work, Which Do Not, and Why

In this paper, we present a combined experimental and theoretical study that systematically evaluates a series of alcohols (i.e., primary, secondary, and tertiary) as coreactants for trimethylaluminum (TMA)-based atomic layer deposition (ALD) of Al 2 O 3 . We employed in situ quartz crystal microbalance techniques and ex situ X-ray photoelectron spectroscopy to probe growth across a range of temperatures from T = 120 to 285 °C. Dispersion-inclusive hybrid density functional theory was employed to identify potential reaction pathways and compute corresponding Gibbs free-energy barriers and rate coefficients. Experimentally on an Al 2 O 3 surface, sustained thin-film growth, where the rate is constant over many cycles, was only observed with tertiary alcohols (tert-butanol and 2-methyl-2-butanol) at elevated temperatures (e.g., T = 285 °C); for tert-butanol specifically, sustained growth also occurred at T = 240 °C, with no sustained growth at or below T = 210 °C. Growth using primary and secondary alcohols decayed to a negligible value after only 2–3 cycles on an Al 2 O 3 surface at all tested temperatures. Intentional trace additions of H 2 O to anhydrous alcohols restored sustained growth but at reduced deposition rates relative to pure H 2 O. Theoretical analysis supports a bimolecular alkoxy β-H elimination mechanism as the primary reaction pathway: initiated by the formation of a bound alkoxy species, the subsequent β-H elimination step is rate-determining, with a barrier that systematically decreases as the degree of the alcohol coreactant is increased from primary to secondary to tertiary. Computed rate coefficients (k) also indicate a strong temperature dependence (with k increasing by ∼4–5 orders of magnitude from T = 120 to 285 °C) and are consistent with the experimental observation that only tertiary alcohols lead to steady thin-film growth at elevated temperatures. This integrated approach establishes why only certain alcohols can sustain Al 2 O 3 ALD and delivers a predictive framework for identifying effective alcohol coreactants.

36 MATERIALS SCIENCE↗
DOE OSTI2026

First-principles simulation of a shocked H-He mixture along the principal Hugoniot

Recent laser-shock experiments on an H–He mixture containing 11% helium (atomic fraction) have suggested the presence of an immiscibility region inside Jupiter. Reflectivity measurements were used as the primary diagnostic of H–He demixing, with discontinuities in the optical reflectivity proposed as a signature of phase separation under conditions relevant to Jupiter's interior. Here, we investigate shock-compressed H–He using ab initio molecular dynamics simulations with optical properties evaluated within the Kubo–Greenwood formalism. The equation of state and ionic configurations were obtained using the thermal Tr 2 SCANL meta-GGA exchange–correlation (XC) functional, while optical properties were computed using the recently developed RS-KDT0 range-separated thermal hybrid XC, which provides state-of-the-art accuracy for band-gap predictions in the warm dense matter regime. The calculated reflectivity shows overall good agreement with experimental measurements; however, no discontinuity is observed at elevated temperatures. Moreover, the reflectivity predictions for the mixed system are consistent with the experimental measurements in the temperature range where the mixture is inferred to be demixed. Furthermore, these results suggest that reflectivity alone may not provide a unique or sensitive diagnostic of H–He demixing at low helium concentrations under these conditions.

Density functional calculations↗
DOE OSTI2024

SEAS Communication Engine: An Extensible, Flexible Wrapper for Co-Simulation Agents

When modeling and analyzing the power grid and other large scale systems, researchers often express scenarios as optimization problems and feed them into advanced software solvers. In order to allow multiple solvers to communicate with each other and share data from different domains, the National Renewable Energy Laboratory (NREL) and associated Department of Energy (DOE) labs have developed a software framework called the Hierarchical Engine for Large-scale Infrastructure Co-Simulation (HELICS). HELICS allows cosimulation via a collection of client libraries for different languages that can be called from the appropriate optimization software. However, these client libraries do not provide a higher level of abstraction beyond reading and writing data off of the shared HELICS bus. In this paper, we describe a new software library called the SEAS Communication Engine that exposes a higher-level API for running cosimulation problems. The SEAS Engine provides a class-based abstraction on top of the Python HELICS client, in order to allow users to implement their domain-specific cosimulations without needing to interact with core HELICS primitives. This will make adoption of HELICS and cosimulation in general easier, by exposing a simpler API. In the second part of the paper, we validate our library on a collection of different simulation examples, including the canonical IEEE 13 Bus Feeder. Lastly, we demonstrate using the SEAS Engine to directly call domain-specific code written in the Julia programming language. Our hope is that this will serve as a template for easily calling software in different programming languages via the SEAS Engine, thereby avoiding code duplication and complexity.

co-simulation↗
DOE OSTI2024

A Model for Hybrid Systems for Production Cost Modeling Studies Considering Ancillary Services

This paper introduces a model for simulating hybrid plants participating in energy and ancillary services for bulk power system studies. The model considers a hybrid plant comprised of a renewable energy source, a thermal power unit, a storage unit, fixed power loads, or any combination of these technologies. The model focuses on Production Cost Modeling (PCM) studies under the assumption of centralized dispatch. We present an example case study to illustrate the use of the model in a single-stage production cost model similar to those conducted by planning agencies. We explore the allocation of behind-the-meter ancillary services products and total energy participation to hybrid plant sub-assets and the resulting impacts on the system's ancillary service allocation. The model is implemented and simulated in a unit commitment problem in the RTS test system, which was modified to include a hybrid plant asset.

ancillary services↗
DOE OSTI2024

Distributed Wind-Energy-Based Hybrids

Presentation defining distributed wind-based hybrids and introducing the Hybrid Optimization Performance Platform (HOPP) an open-source tool that helps design and optimize buildable hybrid power plants.

distributed wind-based hybrids↗
DOE OSTI2025

Discovery of hybrid chemical synthesis pathways with DORAnet

Developing efficient tools for discovering novel synthesis pathways is essential to advance chemical production methods that maximize the use of resources and energy. We introduce DORAnet (Designing Optimal Reaction Avenues Network Enumeration Tool), an open-source computational framework that addresses key limitations in current computer-aided synthesis planning (CASP) tools. DORAnet integrates both chemical/chemocatalytic (i.e., non-enzymatic) and enzymatic transformations, enabling the discovery of hybrid synthesis pathways. With 390 expert-curated chemical/chemocatalytic reaction rules and 3606 enzymatic rules derived from MetaCyc, it provides extensive flexibility for synthetic chemists and biotechnologists. The framework features customizable network expansion strategies, advanced filtering, and pathway search, ranking, and visualization tools. Validated against known reaction data, DORAnet successfully identified both established and novel synthesis routes for key industrial chemicals. In a case study involving 51 high-volume targets, DORAnet frequently ranked known commercial pathways among the top three results, demonstrating its practical relevance and ranking accuracy, while also uncovering numerous alternative (hybrid) synthesis pathways that were highly ranked.

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

From Structured Solvents to Hybrid Materials (SS2HM) for Chemically Selective Capture and Electromagnetic Release of CO 2 : Mechanisms, Stability and Interfaces (Final Report)

The goal of this research program was to develop high capacity sorbents amenable for alternative regeneration approaches for direct air capture (DAC) of CO 2 . In particular, the research aimed to develop an understanding of CO 2 binding mechanism, thermal and oxidative stability, and regeneration energetics of functionalized ionic liquids (ILs), deep eutectic solvents (DESs), and porous materials. ILs and DESs are high-dielectric solvents with structural tunability that permits the rational-design for energy-efficient regeneration approaches based on electromagnetic (EM) field and moisture-swing. By further incorporating these solvents into polymeric capsules and other structural supports, multi-scale interfaces for targeted CO 2 and energy transfers were achieved. Aspects related to CO 2 capacity, selectivity, stability, dielectric properties, and binding energies were examined through experimental and computational design to identify molecular descriptors to inform future design of structured solvents and hybrid materials for DAC. Enclosed final report details the key findings, science advancements, and workforce development efforts from this project.

36 MATERIALS SCIENCE↗
DOE OSTI2025

AI-Powered Knowledge Graphs for Neuromorphic and Energy-Efficient Computing

The surge in scientific literature obscures breakthroughs and hinders the discovery of new research paths. We propose an artificial intelligence (AI) powered framework using large language models (LLMs) and knowledge graphs (KGs) to automate parts of scientific discovery, focusing on energy-efficient AI circuits. Our hybrid approach combines LLMs, structured data, and ontology-based reasoning to construct a comprehensive knowledge graph that integrates insights across computational neuroscience, spiking neuron models, learning rules, architectural motifs, and neuromorphic device technologies. This multi-domain representation enables the generation of hypotheses that connect biological function with implementable, energy-efficient hardware architectures. Using KG embeddings and graph neural networks, the framework generates hypotheses for novel circuits, validates them through optimization on exascale HPC systems, and with tools like SuperNeuro and Fugu, the most promising designs will be prototyped in hardware. This open-source system aims to accelerate discoveries and bridging neuroscience with hardware innovation, drive collaboration, and unlock new opportunities in low-power AI computing.

Gautam, Ashish [ORNL]↗
DOE OSTI2025

A hybrid neural architecture: Online attosecond x-ray characterization

The emergence of high-repetition-rate x-ray free-electron lasers (XFELs), such as SLAC’s LCLS-II, serves as our canonical example for autonomous controls that necessitate high-throughput diagnostics paired with streaming computational pipelines capable of single-shot analysis with extremely low latency. We present the deterministic characterization with an integrated parallelizable hybrid resolver architecture, a hybrid machine learning framework designed for fast, accurate analysis of XFEL diagnostics using angular streaking-based sinogram images. This architecture integrates convolutional neural networks and bidirectional long short-term memory models to denoise input, identify x-ray sub-spike features, and extract sub-spike relative delays with sub-30 attosecond temporal resolution. Deployed on low-latency hardware, it achieves over 10 kHz throughput with 168.3 μs inference latency, indicating scalability to 14 kHz with field-programmable gate array integration. By transforming regression tasks into classification problems and leveraging optimized error encoding, we achieve high precision with low-latency performance that is critical for real-time streaming event selection and experimental control feedback signals. This represents a key development in real-time control pipelines for next-generation autonomous science, generally, and high repetition-rate x-ray experiments in particular.

Accelerator Physics (physics.acc-ph)↗
DOE OSTI2025

Integrating magnetic Co-nanopillars in a NbN-based VAN thin film as a multifunctional hybrid metamaterial

NbN thin films are considered as a promising alternative candidate to conventional Al superconducting electrodes for Josephson junction devices, superconducting qubits and quantum logic circuits. Herein, by co-growing ferromagnetic Co with superconducting NbN, self-assembled NbN–Co vertically aligned nanocomposite (VAN) thin films have been successfully deposited on a MgO substrates, with NbN in its cubic superconducting phase. The obtained VAN metamaterials exhibit epitaxial growth of NbN with uniformly dispersed Co nanoplates. Further physical property characterization reveals that the NbN–Co VAN presents ferromagnet properties with strong out-of-plane magnetic anisotropy and optical anisotropy. This hybrid metamaterial system could find future applications in superconducting spintronic devices and quantum computing devices.

Zhang, Yizhi [Purdue Univ., West Lafayette, IN (Un↗