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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

New Deformation Mechanisms in Nanocrystalline Nano-porous Small Scale Metals as Defined by Kinetically-driven Microstructures

This report describes recent advances in the design and fabrication of nanostructured metallic pillars with hierarchical microstructures using a novel nanoscale additive manufacturing approach. Through a hydrogel-infusion-based two-photon lithography (TPL) method, we successfully fabricated 3D nickel nanopillars exhibiting both nanocrystalline and nanoporous features. The resulting “bamboo-like” internal architecture comprises 30–50 nm grains and voids with similarly scaled pores. These geometrically tunable pillars, with diameters ranging from ~130 to 550 nm, serve as an ideal platform for probing deformation mechanisms in structurally heterogeneous metals at the nanoscale.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Optimizing Error-Bounded Lossy Compression for Scientific Data on GPUs

Error-bounded lossy compression is a critical technique for significantly reducing scientific data volumes. With ever-emerging heterogeneous high-performance computing (HPC) architecture, GPU-accelerated error-bounded compressors (such as CUSZ and cuZFP) have been developed. However, they suffer from either low performance or low compression ratios. To this end, we propose CUSZ+ to target both high compression ratios and throughputs. We identify that data sparsity and data smoothness are key factors for high compression throughputs. Our key contributions in this work are fourfold: (1) We propose an efficient compression workflow to adaptively perform run-length encoding and/or variable-length encoding. (2) We derive Lorenzo reconstruction in decompression as multidimensional partial-sum computation and propose a fine-grained Lorenzo reconstruction algorithm for GPU architectures. (3) We carefully optimize each of CUSZ kernels by leveraging state-of-the-art CUDA parallel primitives. (4) We evaluate CUSZ+ using seven real-world HPC application datasets on V100 and A100 GPUs. Experiments show CUSZ+ improves the compression throughputs and ratios by up to 18.4x and 5.3x, respectively, over CUSZ on the tested datasets.

Tian, Jiannan↗
DOE OSTI2025

Secure API-Driven Research Automation to Accelerate Scientific Discovery

The Secure Scientific Service Mesh (S3M) provides API-driven infrastructure to accelerate scientific discovery through automated research workflows. By integrating near real-time streaming capabilities, intelligent workflow orchestration, and fine-grained authorization within a service mesh architecture, S3M enables secure and flexible programmatic access to high performance computing (HPC) resources. This framework allows intelligent agents and experimental facilities to dynamically provision resources and execute complex workflows, accelerating experimental lifecycles, and enabling AI-augmented autonomous science. S3M establishes a modern foundation for scientific computing infrastructure that significantly reduces traditional barriers between researchers, computational resources, and experimental facilities.

Skluzacek, Tyler [ORNL] (ORCID:0000000322424931)↗
DOE OSTI2020

The genetic architecture of biomechanical traits in sorghum

Sorghum bicolor (L.) Moench is the fifth most commonly grown cereal crop worldwide with unrivaled drought tolerance compared with other cereal crops. Drought and heat tolerance and high biomass yield potential make sorghum a promising bioenergy crop. However, stem lodging is a significant problem that results in substantial yield losses. Stem biomechanical traits influence the mechanical stability of crops and breeding for desirable biomechanical traits may result in improved lodging resistance. In this study, we report the identification of quantitative trait loci (QTL) for stem mechanical and morphological traits in three recombinant inbred line (RIL; populations from Tx623/Rio, Tx623/Della, and Tx631/Della) crosses between elite grain and sweet sorghum parents. The genetic architecture of stem biomechanical traits in the three RIL populations is multigenic and pleiotropic. Additionally, eight QTL affecting mechanical and morphological traits were detected; two main effects of these QTL were consistently found in all populations and colocated with previously identified dwarfing genes Dw1 and Dw3. These results indicate that dwarfing genes affect the mechanical properties of sorghum stems and their lodging resistance, while also having demonstrable effects on stem morphology. The identification of these QTL provides new opportunities for improving stem lodging resistance via genomics-assisted breeding.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

VTK-m: Visualization for the Exascale Era and Beyond

A recent trend in modern high-performance computing is the increasing use of hybrid architectures, where the vast majority of performance comes from accelerators. Modern accelerators are based on Graphics Processing Units (GPU) that contain many low power cores that in their aggregate provides an extremely high computation rate. Current and future CPU processors are requiring more explicit parallelism as each successive version of the hardware packs in more cores, and technologies like hyperthreading and vector operations require even more parallel processing to leverage each core’s full potential. As an example, the Frontier supercomputer installed at Oak Ridge National Laboratories recently hit a record breaking 1.1 exaflops1 on the LINPACK HPC benchmark [Shoemaker 2022]. The system contains 37632 AMD MI250x GPUs which requires more than half a billion threads to keep the system fully utilized [Khizeran 2022].VTK-m is a toolkit of scientific visualization algorithms for these emerging processor architectures. VTK-m supports the fine-grained concurrency for data analysis and visualization algorithms required to drive extreme scale computing by providing abstract models for data and execution that can be applied to a variety of algorithms across many different processor architectures.

Bolstad, Mark↗
DOE OSTI2023

VTK-m v. 2.0

VTK-m is a toolkit of scientific visualization algorithms for emerging processor architectures. VTK-m supports the fine-grained concurrency for data analysis and visualization algorithms required to drive extreme scale computing by providing abstract models for data and execution that can be applied to a variety of algorithms across many different processor architectures.

Moreland, Kenneth [Oak Ridge National Lab. (ORNL),↗
DOE OSTI2020

ECP ST Capability Assessment Report (CAR) for VTK-m (FY20)

The ECP/VTK-m project is providing the core capabilities to perform scientific visualization on Exascale architectures. The ECP/VTK-m project fills the critical feature gap of performing visualization and analysis on processors like graphics-based processors. The results of this project will be delivered in tools like ParaView, Vislt, and Ascent as well as in stand-alone form. Moreover, these projects are depending on this ECP effort to be able to make effective use of ECP architectures. One of the biggest recent changes in high-performance computing is the increasing use of accelerators. Accelerators contain processing cores that independently are inferior to a core in a typical CPU, but these cores are replicated and grouped such that their aggregate execution provides a very high computation rate at a much lower power. Current and future CPU processors also require much more explicit parallelism. Each successive version of the hardware packs more cores into each processor, and technologies like hyper threading and vector operations require even more parallel processing to leverage each core's full potential. VTK-m is a toolkit of scientific visualization algorithms for emerging processor architectures. VTK-m supports the fine-grained concurrency for data analysis and visualization algorithms required to drive extreme scale computing by providing abstract models for data and execution that can be applied to a variety of algorithms across many different processor architectures. The ECP/VTK-m project is building up the VTK-m codebase with the necessary visualization algorithm implementations that run across the varied hardware platforms to be leveraged at the Exascale. We will be working with other ECP projects, such as ALPINE, to integrate the new VTK-m code into production software to enable visualization on our HPC systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Tuning the mechanical properties of nanoglass-metallic glass composites with brick and mortar designs

We use molecular dynamics simulations to characterize the tensile deformation and failure of metallic glass (MG)-nanoglass (NG) composites designed with a brick and mortar architecture consisting of a 3 nm-grain-size NG matrix and a MG second phase. Results show that the overall strength of the matrix is significantly improved by the second phase without sacrificing the large NG matrix ductility. The mechanical synergy of the two phases is optimized by arranging the MG bricks in a staggered way, which effectively delocalizes the plastic deformation, hindering the buildup of local stress hot spots and the generation of critical shear bands.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Long-Range Lamellar Alignment in Diblock Bottlebrush Copolymers via Controlled Oscillatory Shear

A simple strategy is presented to achieve well-ordered nanostructures in microphase-separated diblock bottlebrush copolymers (dbBB) for potential opportunities in nanotechnology that require large grain sizes and directed orientation. The unique architecture and relaxation processes of dbBBs offer a pathway to enhanced dynamic ordering and reorientation over macroscopic scales. Here, controlled shear is used to align lamellar domains. The high molecular mobility of dbBBs in the melt state, as shown by linear and nonlinear viscoelastic characterization, supports this alignment process. When subjected to large-amplitude oscillatory shear at controlled frequency (ω), strain amplitude (γ), and temperature (T shear ), microphase-separated lamellae transition into highly aligned states as determined by small-angle X-ray scattering (SAXS) and transmission electron microscopy (TEM). High throughput synchrotron SAXS is used to rapidly characterize microphase separation and lamellar alignment across the entire bulk sample. Lamellae align parallel to shear planes with an orientation order parameter (S) approaching S = 0.8 over a large cubic volume (V ~ mm 3 ).

36 MATERIALS SCIENCE↗
DOE OSTI2023

Two Mesoporous Domains Are Better Than One for Catalytic Deconstruction of Polyolefins

Catalytic hydrogenolysis of polyolefins into valuable liquid, oil, or wax-like hydrocarbon chains for second-life applications is typically accompanied by the hydrogen-wasting co-formation of low value volatiles, notably methane, that increase greenhouse gas emissions. Catalytic sites confined at the bottom of mesoporous wells, under conditions in which the pore exerts the greatest influence over the mechanism, are capable of producing less gases than unconfined sites. A new architecture was designed to emphasize this pore effect, with the active platinum nanoparticles embedded between linear, hexagonal mesoporous silica and gyroidal cubic MCM-48 silica (mSiO 2 /Pt/MCM-48). This catalyst deconstructs polyolefins selectively into ~C 20 –C 40 paraffins and cleaves C–C bonds at a rate (TOF = 4.2 ± 0.3 s –1 ) exceeding that of materials lacking these combined features while generating negligible volatile side products including methane. The time-independent product distribution is consistent with a processive mechanism for polymer deconstruction. In contrast to time- and polymer length-dependent products obtained from non-porous catalysts, mSiO 2 /Pt/MCM-48 yields a C 28 -centered Gaussian distribution of waxy hydrocarbons from polyolefins of varying molecular weight, composition, and physical properties, including low-density polyethylene, isotactic polypropylene, ultrahigh-molecular-weight polyethylene, and mixtures of multiple, post-industrial polyolefins. Here, coarse-grained simulation reveals that the porous-core architecture enables the paraffins to diffuse away from the active platinum site, preventing secondary reactions that produce gases.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Efficient exascale discretizations: High-order finite element methods

Efficient exploitation of exascale architectures requires rethinking of the numerical algorithms used in many large-scale applications. These architectures favor algorithms that expose ultra fine-grain parallelism and maximize the ratio of floating point operations to energy intensive data movement. One of the few viable approaches to achieve high efficiency in the area of PDE discretizations on unstructured grids is to use matrix-free/partially assembled high-order finite element methods, since these methods can increase the accuracy and/or lower the computational time due to reduced data motion. In this paper we provide an overview of the research and development activities in the Center for Efficient Exascale Discretizations (CEED), a co-design center in the Exascale Computing Project that is focused on the development of next-generation discretization software and algorithms to enable a wide range of finite element applications to run efficiently on future hardware. CEED is a research partnership involving more than 30 computational scientists from two US national labs and five universities, including members of the Nek5000, MFEM, MAGMA and PETSc projects. We discuss the CEED co-design activities based on targeted benchmarks, miniapps and discretization libraries and our work on performance optimizations for large-scale GPU architectures. We also provide a broad overview of research and development activities in areas such as unstructured adaptive mesh refinement algorithms, matrix-free linear solvers, high-order data visualization, and list examples of collaborations with several ECP and external applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

ExaSGD: 2022 Kernel Thrust Activities

The Kernel Thrust milestone ADSE22-407 covers the development of device-capable optimization algorithms and solvers technologies required by the ExaSGD project’s software stack in order to solve security-constrained alternating current optimal power flow (SC-ACOPF) problems on emerging exascale architectures. To this extent, in FY22 the main objective of the Kernel Thrust was (i) provide sparse optimization solver that runs efficiently on hardware accelerator devices (i.e., NVIDIA and AMD GPUs) to perform intra-node computations, (ii) strengthen the reliability and increase the performance of the mixed-dense sparse (MDS) solver of HiOp for deployment on the FY22 target architectures, Summit and Crusher, and (iii) increase performance by improving the mathematical algorithm and refining the parallel MPI-based implementation of the coarse-grain parallel solver HiOp-PriDec for capabilities deployment on the FY22 target architectures, Summit and Crusher. This document presents the developments and contributions done by the Kernels Thrust Team in FY22 toward completion of the above-mentioned objectives. These contributions progressed along four main development (sub)thrusts: (1) Design and implementation of a sparse optimization solver for use on hardware accelerators; (2) Improvement of the mathematical algorithm and of the parallel implementation of HiOp-PriDec to ensure readiness and efficient coarse-grain parallelism for FY23 target exascale machine; and (3) Support Software and Application Development Thrusts of the exaSGD project in their deployment of the project’s software stack on AMD- and NVIDIA-based architectures. The development of the sparse optimization solver (thrust 1 above) was new in FY22 and resulted in a new sparse solver in HiOp (available as of version 0.6). The second development thrust was a continuation of the efforts from FY21 and improved the mathematical algorithm and the communication strategy of the HiOp-PriDec solver. The last developement thrust is a large collaborative effort. Namely, the project’s teams from multiple labs (LLNL, PNNL, ORNL, and NREL) performed large-scale demonstration of the ExaSGD software stack, namely the optimization solvers of HiOp interfaced with the modeling front-end ExaGO and the stochastic sampler PowerScenarios. These demonstration efforts solved large-scale instances of the SC-ACOPF challenge problem of medium network sizes (10, 000-bus system) and large number of contingencies on Summit (NVIDIA accelerators) and Crusher (AMD accelerators) systems at ORNL.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Understanding the Influence of Chain Architecture on the Transport Quantities of Polymer Electrolytes with Covalently Bonded Anions

Here, we use a combination of experiments and coarse-grained molecular dynamics simulations to elucidate the structure–property relationships in polymer electrolytes obtained by the copolymerization of poly(vinyl ethylene carbonate─lithium styrene bis(trifluoromethanesulfonyl)imide) or p(VEC-LiSTFSI). Experiments show that the conductivity reduces with increasing anion (i.e., STFSI) fraction on the chain, and the cation transference number (t + ) is found to be dependent on the anion fraction. Furthermore, a significant fraction of unpolymerized VEC monomers are observed. Since it is inherently difficult to experimentally control the chain architecture and the amount of unpolymerized VEC in these systems, we perform coarse-grained molecular dynamics simulations on model polymer systems with different chain architectures to mimic the plausible experimental systems. Specifically, we look at the differences in transference numbers arising from (i) a random copolymer of VEC and STFSI monomers; (ii) a blend of VEC-STFSI copolymer with VEC monomers; and (iii) a ternary blend of the VEC homopolymer, STFSI homopolymers, and VEC monomers. The ternary blend model demonstrates the closest resemblance with the experimental transference numbers and diffusivities. The lithium diffusivity obtained from the coarse-grained models with VEC monomers (plasticizers) is about 1.5 times that of the model without VEC monomers, showing that the plasticizing effect of VEC monomers is modest. We rationalize the experimental observations based on aggregate and cluster analyses obtained from molecular simulations. This work reveals that polymer electrolyte chain architecture and plasticizers can critically influence the transport properties, and these parameters should be considered when designing single ion conducting polymeric electrolytes.

cluster distribution↗
DOE OSTI2024

Integrating multi-modal remote sensing, deep learning, and attention mechanisms for yield prediction in plant breeding experiments

In both plant breeding and crop management, interpretability plays a crucial role in instilling trust in AI-driven approaches and enabling the provision of actionable insights. The primary objective of this research is to explore and evaluate the potential contributions of deep learning network architectures that employ stacked LSTM for end-of-season maize grain yield prediction. A secondary aim is to expand the capabilities of these networks by adapting them to better accommodate and leverage the multi-modality properties of remote sensing data. In this study, a multi-modal deep learning architecture that assimilates inputs from heterogeneous data streams, including high-resolution hyperspectral imagery, LiDAR point clouds, and environmental data, is proposed to forecast maize crop yields. The architecture includes attention mechanisms that assign varying levels of importance to different modalities and temporal features that, reflect the dynamics of plant growth and environmental interactions. The interpretability of the attention weights is investigated in multi-modal networks that seek to both improve predictions and attribute crop yield outcomes to genetic and environmental variables. This approach also contributes to increased interpretability of the model's predictions. The temporal attention weight distributions highlighted relevant factors and critical growth stages that contribute to the predictions. The results of this study affirm that the attention weights are consistent with recognized biological growth stages, thereby substantiating the network's capability to learn biologically interpretable features. Accuracies of the model's predictions of yield ranged from 0.82-0.93 R 2 ref in this genetics-focused study, further highlighting the potential of attention-based models. Further, this research facilitates understanding of how multi-modality remote sensing aligns with the physiological stages of maize. The proposed architecture shows promise in improving predictions and offering interpretable insights into the factors affecting maize crop yields, while demonstrating the impact of data collection by different modalities through the growing season. By identifying relevant factors and critical growth stages, the model's attention weights provide valuable information that can be used in both plant breeding and crop management. The consistency of attention weights with biological growth stages reinforces the potential of deep learning networks in agricultural applications, particularly in leveraging remote sensing data for yield prediction. To the best of our knowledge, this is the first study that investigates the use of hyperspectral and LiDAR UAV time series data for explaining/interpreting plant growth stages within deep learning networks and forecasting plot-level maize grain yield using late fusion modalities with attention mechanisms.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Flexible and efficient perovskite quantum dot solar cells via hybrid interfacial architecture

All-inorganic CsPbI 3 perovskite quantum dots have received substantial research interest for photovoltaic applications because of higher efficiency compared to solar cells using other quantum dots materials and the various exciting properties that perovskites have to offer. These quantum dot devices also exhibit good mechanical stability amongst various thin-film photovoltaic technologies. We demonstrate higher mechanical endurance of quantum dot films compared to bulk thin film and highlight the importance of further research on high-performance and flexible optoelectronic devices using nanoscale grains as an advantage. Specifically, we develop a hybrid interfacial architecture consisting of CsPbI 3 quantum dot/PCBM heterojunction, enabling an energy cascade for efficient charge transfer and mechanical adhesion. The champion CsPbI3 quantum dot solar cell has an efficiency of 15.1% (stabilized power output of 14.61%), which is among the highest report to date. Building on this strategy, we further demonstrate a highest efficiency of 12.3% in flexible quantum dot photovoltaics.

14 SOLAR ENERGY↗
DOE OSTI2025

Nano-Engineered Interfaces in Dual-Layer Electrodes for Protonic Ceramic Cells with Enhanced Stability and Kinetics

Enhancing interfacial stability and charge transfer in protonic ceramic cells (PCCs) remains a critical challenge, as structural degradation and interfacial resistance often compromise durability and efficiency. Here, we report a nanoengineered dual-layer oxygen electrode architecture designed to address these limitations by introducing a fine-grained nanoparticle interfacial contact layer beneath a porous catalytic backbone. The nanoscale powders, through enhanced sintering activity, densify into a robust interfacial layer that promotes strong chemical bonding, uniform adhesion, and continuous ionic/electronic pathways with the BCZYYb electrolyte. This hierarchical architecture mitigates delamination, redistributes mechanical stress, and establishes efficient charge and mass transport channels without relying on corrosive surface treatments. Electrochemical evaluation demonstrates that the dual-layer design markedly reduces interfacial polarization resistance and accelerates electrode kinetics. Compared to the single-layer counterpart, the architecture achieves a peel strength of 44.53 N/cm 2 , a 40% improvement in peak power density (0.96 W cm –2 at 600 °C), and a 130% enhancement in electrolysis current density (4.78 A cm –2 at 1.57 V). Faradaic efficiency remains as high as 88% under high steam concentrations, underscoring minimal charge loss during practical operation. Notably, the electrode retains stability across 450–600 °C and under transient voltage cycling, with impedance spectra confirming suppressed interfacial resistance growth over prolonged use. These results highlight nanoscale interface engineering as a powerful route to enhance both mechanical robustness and electrochemical kinetics in PCCs. The demonstrated scalability and durability of this architecture provide a versatile platform for advancing solid-state electrochemical systems, including reversible fuel cells and high-efficiency hydrogen production technologies.

Faradaic efficiency↗
DOE OSTI2022

Root system architecture in cereals: progress, challenges and perspective

We report roots are essential multifunctional plant organs involved in water and nutrient uptake, metabolite storage, anchorage, mechanical support, and interaction with the soil environment. Understanding of this ‘hidden half’ provides potential for manipulation of root system architecture (RSA) traits to optimize resource use efficiency and grain yield in cereal crops. Unfortunately, root traits are highly neglected in breeding due to the challenges of phenotyping, but could have large rewards if the variability in RSA traits can be fully exploited. Until now, a plethora of genes have been characterized in detail for their potential role in improving RSA. The use of forward genetics approaches to find sequence variations in genes underpinning desirable RSA would be highly beneficial. Advances in computer vision applications have allowed image-based approaches for high-throughput phenotyping of RSA traits that can be used by any laboratory worldwide to make progress in understanding root function and dissection of the genetics. At the same time, the frontiers of root measurement include non-invasive methods like X-ray computer tomography and magnetic resonance imaging that facilitate new types of temporal studies. Root physiology and ecology are further supported by spatiotemporal root simulation modeling. The discovery of component traits providing improved resilience and yield advantage in target environments is a key necessity for mainstreaming root-based cereal breeding. The integrated use of pan-genome resources, now available in most cereals, coupled with new in-field phenotyping platforms has the potential for precise selection of superior genotypes with improved RSA.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

An end-to-end workflow for executing a classically bootstrapped variational quantum algorithm on an academic quantum computer

Academic quantum computing platforms often face unique challenges in executing quantum workloads due to fragmented software environments and limited engineering support. Unlike commercial ecosystems, academic devices typically evolve without full-stack integration in mind, making it difficult to run complex applications—such as variational quantum algorithms (VQA)—reliably and efficiently. Issues such as incompatible software layers and lack of automated job management significantly increase the overhead of theory-experiment collaboration. To address these challenges, we develop a modular, end-to-end workflow that decouples application-layer code from low-level hardware control, automates circuit submission and result collection, and supports fine-grained circuit-level job scheduling and recovery. The architecture employs a dual-end application programming interface (API) design, enabling robust operation across unstable or resource-constrained hardware backends. For practical use, the framework is lightweight and user-friendly, allowing rapid prototyping of full-stack workflows using basic Python tools. We validate this workflow on a high-fidelity trapped-ion quantum computer by demonstrating a variational quantum eigensolver (VQE) experiment with a classically bootstrapped ansatz initialization technique. The system successfully executed over 60,000 circuits across multiple molecular test cases with minimal human intervention, highlighting the framework’s effectiveness in enabling reproducible, resilient quantum experimentation in academic settings.

Clifford↗