Search NASA⌕ Search

SEARCH · Search NASA

Results for “Structure optimization”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

HPC Digital Twins for Evaluating Scheduling Policies, Incentive Structures and their Impact on Power and Cooling

Schedulers are critical for optimal resource utilization in high-performance computing. Traditional methods to evaluate sched- ulers are limited to post-deployment analysis, or simulators, which do not model associated infrastructure. In this work, we present the first-of-its-kind integration of scheduling and digital twins in HPC. This enables what-if studies to understand the impact of parameter configurations and scheduling decisions on the physical assets, even before deployment, or regarching changes not easily realizable in production. We (1) provide the first digital twin framework extended with scheduling capabilities, (2) integrate various top-tier HPC systems given their publicly available datasets, (3) implement extensions to integrate external scheduling simulators. Finally, we show how to (4) implement and evaluate incentive structures, as- well-as (5) evaluate machine learning based scheduling, in such novel digital-twin based meta-framework to prototype scheduling. Our work enables what-if scenarios of HPC systems to evaluate sustainability, and the impact on the simulated system.

Maiterth, Matthias [ORNL] (ORCID:000000018698460X)↗
DOE OSTI2025

Mechanistic insights into superionic thioarsenate argyrodite solid electrolytes via machine learning interatomic potentials

The lithium argyrodite sulfide solid electrolyte Li 6 PS 5 Cl has attracted considerable interest for all-solid-state batteries owing to its high ionic conductivity, which can be further enhanced through ionic substitution. Although a variety of substitutions have been investigated, thioarsenate argyrodites remain comparatively underexplored. Here, we systematically investigate the phase stability and Li-ion conduction mechanisms in superionic Br-incorporated thioarsenate argyrodites using first-principles calculations and molecular dynamics simulations based on machine learning interatomic potentials (MLIPs). Systematic variation of S/Br site inversion reveals that an optimal degree of anion disorder significantly enhances inter-cage connectivity and facilitates long-range Li-ion diffusion. Configurational entropy serves as an effective quantitative descriptor of anion disorder, exhibiting a strong correlation with ionic conductivity. While greater anion disorder induced by site inversion and higher Br content enhances ionic conductivity up to 50 mS cm −1 , it simultaneously reduces structural stability. This trade-off results in an optimal window in which a moderate level of disorder yields conductivities exceeding 20 mS cm −1 while maintaining synthetic feasibility. In conclusion, this work highlights the reliability and efficiency of MLIPs for elucidating ion-transport mechanisms and accelerating the design of novel superionic argyrodites.

Jang, Myeongcho [Korea Institute of Science and Te↗
DOE OSTI2025

Structural evolution and magnetic hardness of (Sm,Zr)(Fe,Co,Ti) 12 alloy particles via reduction-diffusion

The quest for achieving high coercivity in Sm(Fe,Co,Ti) 12 alloys, despite their inherent strong magnetocrystalline anisotropy, has posed significant challenges. Recently, (Sm,Zr)(Fe,Co,Ti) 12 monocrystalline particles have exhibited coercivity μ 0 H c > 1.2 T, showcasing promising prospects and significant potential for both manufacturing and research endeavors. This study delves into the structural evolution of (Sm,Zr)(Fe,Co,Ti) 12 (1:12) alloy particles made via the calciothermic reduction-diffusion synthesis process as influenced by the molar ratios of Ca atoms to O 2- ions (Ca/O), annealing time and annealing temperature. Critical insight that informs conditions to optimize the magnetic response is gained via systematic experimentation and advanced electron microscopy. Complex structural features, including core-shell morphologies and intricate multiphase compositions within individual particles, are unveiled. An optimal Ca/O ratio of 1.30 produces particles with a coercivity up to μ 0 H c = 1.63 T, while higher Ca/O ratios induce the formation of a Sm-rich TbCu 7 -type (1: 7 ) phase, which only partially transforms into the desired 1:12 phase during annealing. Persistent remnants of the 1:7 phase locally impact atomic structure, particle morphology, and coercivity. Furthermore, these findings underscore the complex interplay between synthesis parameters, resulting structures, and magnetic properties, informing the design and optimization of high-performance permanent magnets comprised of the (1:12) compound.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Bench-Scale Testing of Monolithic PPI Structured Contactors for Direct Air Capture of CO 2

The overall project objective was to develop, optimize and bench-scale test the integrated embodiment of a leading direct air capture (DAC) sorbent composition, linear-poly(propylenimine) (l-PPI), in a structured material system, specifically a monolithic contactor for achieving low pressure drop, to justify its further scale-up in a subsequent program. The project team was led by CORMETECH, a leading monolithic gas/solid contactor manufacturer for environmental applications, and included Global Thermostat, a well-known DAC start-up, and Georgia Tech, a prominent US academic institution in carbon capture and DAC. The monolithic contactor for l-PPI in this project employed an advanced, cost-effective fabrication technique from CORMETECH, namely a porous monolith substrate directly impregnated with the amine, an approach contrasting to the traditional wash-coating methods used for activating monoliths. The l-PPI monolithic contractor is targeted for use in DAC systems such as the Global Thermostat DAC process, which involves the cyclic operation of ambient air flow over a monolithic amine contactor followed by steam-mediated thermal desorption and CO 2 collection, maximizing volumetric productivity while reducing the auxiliary power required to capture CO 2 from air. The Global Thermostat DAC process currently utilizes poly(ethyleneimine) (PEI) materials as baseline sorbents, which are subject to oxidative degradation and slow capacity fade at the elevated temperature required for efficient CO 2 removal. Georgia Tech, with Global Thermostat, had discovered in lab-scale efforts that l-PPI had superior resistance to oxidative degradation compared to PEI, with similar CO 2 adsorption capacity. While the novel l-PPI sorbent offers significant advantages over PEI for DAC, including potential design simplification and increased process efficiency, it is not commercially available, and prior to this project, had not yet been evaluated on the bench-scale in a structured contactor.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Grain boundary engineering for efficient and durable electrocatalysis

Grain boundaries in noble metal catalysts have been identified as critical sites for enhancing catalytic activity in electrochemical reactions such as the oxygen reduction reaction. However, conventional methods to modify grain boundary density often alter particle size, shape, and morphology, obscuring the specific role of grain boundaries in catalytic performance. This study addresses these challenges by employing gold nanoparticle assemblies to control grain boundary density through the manipulation of nanoparticle collision frequency during synthesis. We demonstrate a direct correlation between increased grain boundary density and enhanced two-electron oxygen reduction reaction activity, achieving a significant improvement in both specific and mass activity. Additionally, the gold nanoparticle assemblies with high grain boundary density exhibit remarkable electrochemical stability, attributed to boron segregation at the grain boundaries, which prevents structural degradation. This work provides a promising strategy for optimizing the activity, selectivity, and stability of noble metal catalysts through precise grain boundary engineering.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Designing Moderately‐Solvating Electrolytes for High‐Performance Lithium–Sulfur Batteries

New electrolytes are critical for high‐energy lithium (Li)–sulfur (S) batteries (LSBs) to ensure their stability against Li metal anode and polysulfides (PSs) shuttling which hinder the large‐scale application of LSBs. In this study, the design principle of moderately solvating electrolytes (MSEs) for LSBs is demonstrated by using a multiple‐solvent system comprising of a highly solvating solvent, a weakly solvating solvent, and a non‐solvating solvent to create a well‐balanced electrolyte system. This resulting electrolyte significantly improves the cycle life of LSBs, achieving 300 cycles, which is twice as long as that of similar cells with the conventional electrolyte and it also ensures stable calendar life for at least seven months. The optimal MSE forms robust passivation layers enhancing the structural integrity of both S and Li metal electrodes after cycling. These virtues effectively hinder parasitic side reactions and self‐discharge behavior of LSBs. This electrolyte design principle is versatile and can be applied to other battery chemistries, providing a potential path toward the development of a more efficient and stable battery system. By addressing key challenges such as the instability of electrodes and shuttling of polysulfides, this electrolyte approach offers promising solutions for advancing LSB technology.

25 ENERGY STORAGE↗
DOE OSTI2025

Understanding Structural and Compositional Evolution during NMC Cathode Direct Recycling via Solid-State NMR

Recycling end-of-life lithium-ion batteries (LIBs) to recover high-value cathode materials such as LiNixMnyCozO2 (NMC) is driven by economical, geopolitical, and sustainability needs. There has been recent interest in direct recycling methods to improve efficiency and recovery of materials, including ionothermal, hydothermal, solid-state, or redox mediator methods. In conjunction with recycling process development, detailed structural characterization is necessary in order to understand the mechanisms and efficacy of cathode recycling steps. Solid-state nuclear magnetic resonance (NMR) spectroscopy is a unique tool that can probe Li coordination, bulk and surface environments, and transition metal ordering in recycled and upcycled NMC cathodes. Here, 6,7Li, 1H, and 19F NMR spectroscopy to probe structural and compositional changes as well as surface impurities that may form during each step in NMC direct recycling is utilized. During relithiation, Li reinsertion into the NMC lattice is observed. During upcycling, where the goal is to increase the Ni content in the NMC, incorporation of Ni-rich phases into the bulk Li environment is observed. Surface impurities formed during processing were also identified. These studies provide valuable information for optimizing recycling processes to reach targeted cathode composition and structure that can enable electrochemical performance comparable to or better than pristine materials.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Hypersonic Jets of Detonation Products in the Hydrodynamic Collapse of Macroscopic Voids

Localizing the energetic output from detonation waves has been a long-standing challenge in applied detonation physics. Here, energy localization is achieved via machined millimeter scale voids in pressed samples of PBX 9501, an HMX (1,3,5,7-Tetranitro-1,3,5,7-tetrazocane)-based plastic bonded explosive. A main mechanism of energy localization in these systems, the formation of hydrodynamic jets of dense product gases, is characterized experimentally using a semicylindrical geometry in witness plate impact experiments and streak imaging of the jet propagating into the air. The distance at which the jet is optimally developed is identified and the supersonic flow structure in the vicinity of this feature is explored using hydrocode simulations. This analysis found that most of the kinetic energy of the hydrodynamic jet arises from pressure gradients induced by geometrically mediated squeeze flow lateral to the direction of detonation propagation. This work presents a new development in the control of energetic output from detonation waves and applications to detonation wave shaping are discussed.

42 ENGINEERING↗
DOE OSTI2024

QRCODE: Massively parallelized real-time time-dependent density functional theory for periodic systems

We present a new software module, QRCODE (Quantum Research for Calculating Optically Driven Excitations), for massively parallelized real-time time-dependent density functional theory (RT-TDDFT) calculations of periodic systems in the open-source Qbox software package. Our approach utilizes a custom implementation of a fast Fourier transformation scheme that significantly reduces inter-node message passing interface (MPI) communication of the major computational kernel and shows impressive scaling up to 16,344 CPU cores. In addition to improving computational performance, QRCODE contains a suite of various time propagators for accurate RT-TDDFT calculations. As benchmark applications of QRCODE, we calculate the current density and optical absorption spectra of hexagonal boron nitride (h-BN) and photo-driven reaction dynamics of the ozone-oxygen reaction. We also calculate the second and higher harmonic generation of monolayer and multi-layer boron nitride structures as examples of large material systems. Our optimized implementation of RT-TDDFT in QRCODE enables large-scale calculations of real-time electron dynamics of chemical and material systems with enhanced computational performance and impressive scaling across several thousand CPU cores.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Towards automated and real-time multi-object detection of anguilliform fishes from sonar data using YOLOv8 deep learning algorithm

Eels (Anguilla spp.), including American eels (Anguilla rostrata), European eels (Anguilla anguilla), and Japanese eels (Anguilla japonica), are species of critical management and regulatory concern due to their vulnerability to various stressors during downstream migrations. Accurate and efficient detection of migrating eels can improve our understanding of fish behaviors and fish-hydraulic structure interactions, thereby facilitating the design, operation, and optimization of more effective downstream passage facilities from both biological and economic perspectives. However, a real-time, automated framework for detecting migrating eels in real-world applications is currently lacking. Leveraging imaging sonar as a reliable technology for fish passage monitoring, field data are acquired using imaging sonar and then converted to single sonar frames/images for subsequent analysis. In this study, a framework based on the You Only Look Once Version 8 (YOLOv8)-based convolutional neural network is proposed for multi-object detection of eels and non-eel fish using the sonar images after image subtraction and additional wavelet denoising. The results from both training and testing phases demonstrate that the framework's ability can successfully detect both eels and non-eel fish in preprocessed sonar images, achieving F1-scores and mAP@0.50 exceeding 0.84. Additionally, the incorporation of wavelet denoising during preprocessing slightly improve detection performance. Furthermore, the transferability of this framework from eel to lamprey detection is demonstrated to be feasible given the similar morphological characteristics of these two species. Overall, the proposed framework achieves accurate and efficient detection of migrating eels, providing reliable and real-time information that can help conserve vulnerable eel and eel-like populations.

Deep learning↗
DOE OSTI2026

Trends and limits of CO 2 capture in solid and liquid sorbents at standard conditions

Carbon capture and storage (CCS) plays a critical role in achieving climate change mitigation targets, offering a pathway to decarbonize power generation, industrial processes, and heat production while addressing atmospheric CO 2 removal. While CCS technologies are technically advanced, the widespread adoption of 100 % CO 2 capture capacities such as 1 mol of CO 2 /mol of material and 1 g CO 2 /g storage (targeted by the DARPA, Defense Sciences Office, USA Govt.) has raised questions about the feasibility of achieving higher capture capacities. In the context of limiting global warming to 1.5°C, reaching 100 % CO 2 capture capacity is increasingly necessary, with residual emissions requiring complementary carbon dioxide removal (CDR) technologies. This review exclusively focuses on the CO 2 capture capacities of various sorbents under standard conditions, using different evaluation metrics. This study explores the performance of solid and liquid sorbents under standard conditions, analyzing factors including surface area, pore structure, solvent type, and functionalization to identify materials optimized for industrial-scale CCS applications. Emerging sorbents, including ILs, MOFs, COFs, POPs, DES, RCC, hybrid materials, and reactive sorbents, offer significant potential for enhanced selectivity and energy-efficient regeneration. Through a systematic assessment of gravimetric, volumetric, and molar capacities, the study provides insights into material efficiencies and trade-offs, offering guidance on optimizing sorbent selection for specific applications. The research advances understanding of scalable CCS technologies, contributing to global efforts to achieve net-zero emissions and address the pressing challenge of climate change.

Absorption↗
DOE OSTI2024

Convergence of variational Monte Carlo simulation and scale-invariant pre-training

We provide theoretical convergence bounds for the variational Monte Carlo (VMC) method as applied to optimize neural network wave functions for the electronic structure problem. Here, we study both the energy minimization phase and the supervised pre-training phase that is commonly used prior to energy minimization. For the energy minimization phase, the standard algorithm is scale-invariant by design, and we provide a proof of convergence for this algorithm without modifications. The pre-training stage typically does not feature such scale-invariance. We propose using a scale-invariant loss for the pretraining phase and demonstrate empirically that it leads to faster pre-training.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Leveraging Large Language Models for Understanding Fundamental Principles of Catalysis

Heterogeneous catalysis presents a distinct challenge for artificial intelligence (AI). Data sets are often small and inconsistently reported, catalyst representations are not standardized, and extracting fundamental knowledge requires integrating performance data, spectroscopic characterizations, and mechanistic models across multiple scales. Language offers a unifying representation across these modalities, making catalysis well suited for leveraging large language models (LLMs). By standardizing how catalytic data is represented, LLMs make dispersed experimental results more accessible to downstream statistical modeling. In this perspective, we focus our discussion around three opportunities where LLMs can significantly contribute to catalysis: (1) text to properties; (2) text to structure; and (3) text to mechanistic models. The discussion is followed by a perspective section on LLM-readiness of data, aligning LLM outputs with scientific correctness, and bridging lab-scale discovery to industrial deployment. Across each area, the most productive applications couple dispersed chemical knowledge with physics-grounded validation to produce verifiable hypotheses and actionable representations.

Catalysts↗
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↗
DOE OSTI2026

Revealing multiscale competing processes in the solid-state synthesis of single-crystalline layered oxide positive electrodes

Solid-state synthesis involves a web of coupled chemical reactions and physical changes that unfold across multiple scales. Efforts to fine-tune its parameters have historically followed heuristic, trial-driven workflows that demand significant time and resources. In this study, we aimed to open this black box by employing multiscale in situ synchrotron imaging and diffraction. Using LiNi 0.5 Mn 0.3 Co 0.2 O 2 battery positive electrode material as a model system and Ba-based sintering aids, we reveal dopant segregation, intergranular mass transport, and porosity evolution as key drivers of single-crystalline particle formation. Notably, we uncovered a dynamic competition between particle-level grain coalescence and atomic-scale cation disordering, both of which are thermally activated yet have opposing impacts on battery performance. These findings highlight the coupled, multiscale nature of structure development and offer a mechanistic basis for optimizing the solid-state synthesis process. This framework provides a path toward more controlled, efficient, and scalable production of high-performance battery positive electrode materials.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Deciphering electrocatalysts with multimodal operando approaches

Here, the electrocatalytic processes of a copper catalyst during nitrate electroreduction are unveiled by correlated operando microscopy and spectroscopy. Catalysts are vital to the modern chemical industry, yet their development has largely relied on trial-and-error approaches. Optimizing catalysts requires a fundamental understanding of their structure–chemical property behaviour under operational conditions. However, operando characterization remains challenging, especially for reactions occurring in the complex and dynamic liquid environments of electrochemical systems. Over the past decade, the rapid development of in situ and operando environmental transmission electron microscopy (ETEM) and microelectromechanical system (MEMS)-based closed-cell holders, enabling environmental studies within the vacuum environment of transmission electron microscopy (TEM), has substantively advanced understanding of heterogeneous catalysis, notably for gas-phase reactions. In situ ETEM enables the direct observation of the catalyst evolution in terms of structure, morphology and chemical state at the nano-to-atomic scale, providing insights into their correlation with catalytic performance.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Alloying multiple halide perovskites on the same sublattice in search of stability and target band gaps

Single-component halide perovskites (HPs) rarely satisfy all the necessary criteria for optoelectronic applications, such as achieving an optimal band gap while maintaining high chemical and structural stability. Alloying halide perovskites has emerged as a promising strategy, not only to enhance stability but also to fine-tune their electronic and optical properties. In this work, we explore multiple degrees of freedom in alloy design, considering different substitution sublattices sites (A, B, or X in ABX3 perovskites), various chemical species (isovalent and hetero-valent elements), and multi-component compositions on a given sublattice. Using first-principles calculations based on density functional theory (DFT), we investigate how compositional variations influence the electronic (band gap) and structural properties (mixing enthalpy) of HP alloys. Our approach employs the polymorphous cell model, allowing full local relaxation which breaks local symmetry while preserving global cubic symmetry—an essential framework for accurately modeling HPs. Our results reveal that X-site mixing (halogen substitution) primarily affects the valence band maximum, allowing target band gap engineering. Additionally, variations in halogen radii introduce internal strain through octahedral distortions, influencing the mixing enthalpy. A-site substitution, while not directly contributing to the band edge states, modifies structural stability via volume effects, indirectly impacting the band gap. B-site alloying plays a dominant role in band gap modulation, leading to either positive or negative band gap bowing. Specifically, isovalent B-site mixing (Sn–Pb) induces strong positive bowing, where the alloy band gap is smaller than the average gap of parent compounds, whereas hetero-valent mixing (Cd–Pb) results in pronounced negative bowing. As an aside, we investigate the competition between the excess energy of disordered alloys vs. that of long-range ordered double perovskites of the same compositions, seeking examples of ordered phases emerging from disordered alloys. Furthermore, our findings provide fundamental insights into the electronic and structural behavior of HP alloys, offering valuable design principles for the development of stable and efficient materials for next-generation photovoltaic and optoelectronic devices.

14 SOLAR ENERGY↗
DOE OSTI2025

Extending JuTrack’s capabilities to the FRIB accelerator to enhance online modeling

JuTrack is a Julia-based accelerator modeling and tracking package that utilizes compiler-level automatic differentiation (AD) to enable fast and accurate derivative calculations. While JuTrack provides a solid foundation for beam dynamics simulations, its capabilities must be extended to support the Facility for Rare Isotopes (FRIB) linac. This includes modeling heavy-ion linac accelerator components such as the liquid-lithium charge stripper, which facilitates efficient acceleration by remove electrons from heavy isotopes, and incorporating multi-charge state acceleration tracking, which allows for charge-dependent beam dynamics. These extensions address challenges such as the beam matching and optimization of multi charge state through various accelerating structures and beam-material interaction modeling while maintaining the auto differentiation capability. This work focuses on adapting JuTrack to incorporate these elements, enhancing its online modeling abilities. We present modifications to JuTrack’s framework and demonstrate their performance in FRIB simulations.

Accelerator Physics↗