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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 OSTI2021

Deep Learning Accelerated Determination of Hydride Locations in Metal Nanoclusters

Although the coordinates of the metal atoms can be accurately determined by X-ray crystallography, locations of hydrides in metal nanoclusters are challenging to determine. In principle, neutron crystallography can be employed to pinpoint the hydride positions, but it requires a large crystal and a neutron source, which prevents its routine use. Here, we present a deep-learning approach that can accelerate determination of hydride locations in single-crystal X-ray structure of metal nanoclusters of different sizes. We demonstrate the efficiency of our method in predicting the most probable hydride sites and their combinations to determine the total structure for two recently reported copper nanoclusters, [Cu 25 H 10 (SPhCl 2 ) 18 ] 3- and [Cu 61 (S t Bu) 26 S 6 Cl 6 H 14 ] + whose hydride locations have not been determined by neutron diffraction. Our method can be generalized and applied to other metal systems, thereby eliminating a bottleneck in atomically precise metal hydride nanochemistry.

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

Fouling behavior of zwitterionic membranes compared to polyamide membranes

Membrane fouling remains a critical bottleneck for reverse osmosis (RO) desalination, driving energy consumption and reducing membrane lifetime. Here, we employ all-atom molecular dynamics simulations to investigate the antifouling behavior of random zwitterionic amphiphilic copolymer (r-ZAC) membranes composed of sulfobetaine methacrylate (SBMA) and allyl methacrylate (AMA), benchmarked against conventional polyamide (PA) RO membranes. Structural and dynamical analyses—including radial distribution functions, coordination numbers, tetrahedral order parameters, vector orientation, and residence-time correlation functions—reveal that r-ZAC surfaces sustain tightly bound, long-lived hydration layers with preserved tetrahedrality and anisotropic water orientation, in sharp contrast to the weak and disordered hydration of PA. Steered molecular dynamics simulations demonstrate that r-ZAC membranes impose substantial free-energy barriers to foulant approach (alginate ≈ 90 kcal/mol, sucrose ≈ 35 kcal/mol, humic acid ≈ 15 kcal/mol), whereas PA membranes exhibit negligible barriers (< 1 kcal/mol) and thermodynamically favorable adsorption. Detailed foulant–surface interaction analyses show that zwitterionic hydration and electrostatic heterogeneity in r-ZAC suppress adhesion, except in the case of amphiphilic humic acid, which exploits multiple binding modes. Together, these results establish molecular-level design principles for antifouling membranes: the combination of zwitterionic hydration, structured interfacial water, and controlled amphiphilic balance in r-ZAC membranes provides superior resistance to organic fouling relative to PA.

Cross-linked polyamide↗
DOE OSTI2025

Spotlight: efficient automated global optimization in rietveld analysis of diffraction data

Performing reliable Rietveld analysis on tens or hundreds of powder diffraction datasets from parametric or time-resolved experiments often poses a bottleneck in extracting meaningful results from the data. While automated analysis of data has recently been demonstrated, high temperature annealing studies, during which phase transformations occur and lattice parameters may change due to repartitioning of elements, are prime examples where automation by a simple phase identification from a database of room temperature structures or automation by sequential refinements is likely to fail. To enable reliable, efficient, automated Rietveld analysis, we present a Python package named Spotlight , building on established Rietveld packages such as MAUD, GSAS , or GSAS-II , which extends the refinement of best fit parameters to a global optimization using an ensemble of optimizers leveraging hierarchical parallel execution on high-performance computing clusters. Spotlight further enables the efficient design of refinement plans through the iterative automated machine-learning of a surrogate for the refinement on which the global optimizations are performed until results from the surrogate converge to the response surface data. We demonstrate Spotlight with the analysis of uranium molybdenum and Ti–6Al–4V datasets, as well as in two open-source tutorials analyzing aluminium oxide and lead sulphate.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Li-Ion Diffusion Correlations in LiAlGeO 4 : Quasielastic Neutron Scattering and Ab Initio Simulation

Here, we investigated the impact of Li stoichiometry and host flexibility on Li + diffusion processes in LiAlGeO 4 at the microscopic level using quasielastic neutron scattering (QENS) and ab initio molecular dynamics (AIMD) simulations. Using sufficiently long AIMD trajectories, we could simulate the observed QENS signal and identify the localized dynamics of Li in crystalline LiAlGeO 4 . Such information is vital to identify the bottleneck of diffusion processes and design materials for battery application. Our AIMD simulations in LiAlGeO 4 reveal that the Li + conductivity can be significantly improved by manipulating the Li stoichiometry and/or host flexibility via amorphization. We determined that excess Li stoichiometry enhances the Coulomb repulsion of neighboring Li sites and softens the host structure to enable faster Li + diffusion along the hexagonal c-axis. In the amorphous structure, random orientations of AlO 4 and GeO 4 polyhedral units create a wide distribution of intersite distances and significantly soften the host structure, greatly enhancing the Li + diffusion. The simulations are used to understand the nature of diffusion, especially the role of the host structure, the possible hopping pathways, and the diffusion behavior in various structures of LiAlGeO 4 .

localized dynamics↗
DOE OSTI2023

Structural basis for breadth development in the HIV-1 V3-glycan targeting DH270 antibody clonal lineage

Antibody affinity maturation enables adaptive immune responses to a wide range of pathogens. In some individuals broadly neutralizing antibodies develop to recognize rapidly mutating pathogens with extensive sequence diversity. Vaccine design for pathogens such as HIV-1 and influenza has therefore focused on recapitulating the natural affinity maturation process. Here, we determine structures of antibodies in complex with HIV-1 Envelope for all observed members and ancestral states of the broadly neutralizing HIV-1 V3-glycan targeting DH270 antibody clonal B cell lineage. These structures track the development of neutralization breadth from the unmutated common ancestor and define affinity maturation at high spatial resolution. By elucidating contacts mediated by key mutations at different stages of antibody development we identified sites on the epitope-paratope interface that are the focus of affinity optimization. Thus, our results identify bottlenecks on the path to natural affinity maturation and reveal solutions for these that will inform immunogen design aimed at eliciting a broadly neutralizing immune response by vaccination.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Brighter, faster, stronger: ultrafast scattering of free molecules

Advances in FEL technologies have contributed remarkably to various scientific fields over the past decade, and ultrafast molecular dynamics is no exception. The ability to probe motions of the molecule via scattering provides uniquely direct structural information, which, when combined with traditional spectroscopic techniques of comparable temporal resolution, paints a holistic movie of the molecular dynamics. This review aims to provide an introduction to the ultrafast scattering of gas-phase molecules, and to identify the key results and technological breakthroughs that advance our acquaintance of ultrafast molecular dynamics, with a particular focus on the achievements in ultrafast molecular dynamics since the first generation of FEL facilities. We pre- sent a brief history of gas-phase ultrafast scattering and the fundamentals of electron- and x-ray scattering, highlighting the complementarity, differences, and bottlenecks of the two experimental scattering methods. Furthermore, we then consider key upgrades in XRS and UED experiments that facilitated the unprecedented spatiotemporal resolution that enabled many of the notable results in the field. Finally, we examine anticipated facility upgrades that address the demand for experimental versatility and enable further developments and exploration.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2024

h5bench: A unified benchmark suite for evaluating HDF5 I/O performance on pre‐exascale platforms

Summary Parallel I/O is a critical technique for moving data between compute and storage subsystems of supercomputers. With massive amounts of data produced or consumed by compute nodes, high‐performant parallel I/O is essential. I/O benchmarks play an important role in this process; however, there is a scarcity of I/O benchmarks representative of current workloads on HPC systems. Toward creating representative I/O kernels from real‐world applications, we have created h5bench , a set of I/O kernels that exercise hierarchical data format version 5 (HDF5) I/O on parallel file systems in numerous dimensions. Our focus on HDF5 is due to the parallel I/O library's heavy usage in various scientific applications running on supercomputing systems. The various tests benchmarked in the h5bench suite include I/O operations (read and write), data locality (arrays of basic data types and arrays of structures), array dimensionality (one‐dimensional arrays, two‐dimensional meshes, three‐dimensional cubes), I/O modes (synchronous and asynchronous). In this paper, we present the observed performance of h5bench executed along several of these dimensions on existing supercomputers (Cori and Summit) and pre‐exascale platforms (Perlmutter, Theta, and Polaris). h5bench measurements can be used to identify performance bottlenecks and their root causes and evaluate I/O optimizations. As the I/O patterns of h5bench are diverse and capture the I/O behaviors of various HPC applications, this study will be helpful to the broader supercomputing and I/O community.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Rapid sensing of hidden objects and defects using a single-pixel diffractive terahertz sensor

Abstract Terahertz waves offer advantages for nondestructive detection of hidden objects/defects in materials, as they can penetrate most optically-opaque materials. However, existing terahertz inspection systems face throughput and accuracy restrictions due to their limited imaging speed and resolution. Furthermore, machine-vision-based systems using large-pixel-count imaging encounter bottlenecks due to their data storage, transmission and processing requirements. Here, we report a diffractive sensor that rapidly detects hidden defects/objects within a 3D sample using a single-pixel terahertz detector, eliminating sample scanning or image formation/processing. Leveraging deep-learning-optimized diffractive layers, this diffractive sensor can all-optically probe the 3D structural information of samples by outputting a spectrum, directly indicating the presence/absence of hidden structures or defects. We experimentally validated this framework using a single-pixel terahertz time-domain spectroscopy set-up and 3D-printed diffractive layers, successfully detecting unknown hidden defects inside silicon samples. This technique is valuable for applications including security screening, biomedical sensing and industrial quality control.

Li, Jingxi (ORCID:0000000165958680)↗
DOE OSTI2026

Charge accumulation and solvation in $β$-NiOOH: Surface chemistry of an OER catalyst from ML-aided simulations

Electrochemical water splitting is a key technology for a sustainable energy transition, providing a route to store surplus electricity from renewable sources. A central bottleneck is the sluggish oxygen evolution reaction (OER), which drives the search for catalysts that are active, stable, and inexpensive enough for large-scale deployment. Within this context, pure and doped NiO x H y combine high activity with low cost, making them prime candidates for alkaline OER. Yet, despite extensive study, the atomistic structure of NiOOH under operando conditions and the associated reaction mechanisms remain debated. Here, we investigate the structural complexity of pure β-NiOOH, the scaffold for its doped derivatives. We systematically investigate the oxidation of the surface adsorbates via proton-coupled electron transfer steps across relevant facets and sites, identifying the most probable sequence of deprotonation events. Our results reveal asymmetric charge accumulation on Wulff-relevant surfaces and show how applied potential can promote morphological restructuring. Explicit solvation is included through machine-learning interatomic potential molecular dynamics of the NiOOH/water interface, which allows us to resolve the hydrophobic and hydrophilic character of different surfaces and the associated interfacial water structure. Together, these insights demonstrate how surface chemistry and solvation jointly govern the stability of NiOOH and the accumulation of surface charge, with possible implications for catalytic performance.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Rubisco Function, Evolution, and Engineering

Carbon fixation is the process by which CO 2 is converted from a gas into biomass. The Calvin–Benson–Bassham cycle (CBB) is the dominant carbon-consuming pathway on Earth, driving >99.5% of the ~120 billion tons of carbon that are converted to sugar by plants, algae, and cyanobacteria. The carboxylase enzyme in the CBB, ribulose-1,5-bisphosphate carboxylase/oxygenase (rubisco), fixes one CO 2 molecule per turn of the cycle into bioavailable sugars. Despite being critical to the assimilation of carbon, rubisco's kinetic rate is not very fast, limiting flux through the pathway. This bottleneck presents a paradox: Why has rubisco not evolved to be a better catalyst? Many hypothesize that the catalytic mechanism of rubisco is subject to one or more trade-offs and that rubisco variants have been optimized for their native physiological environment. Here, we review the evolution and biochemistry of rubisco through the lens of structure and mechanism in order to understand what trade-offs limit its improvement. We also review the many attempts to improve rubisco itself and thereby promote plant growth.

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

An Open-Source Parallel EMT Simulation Framework

As the integration level of inverter-based resources (IBRs) increases, ensuring the reliable operation of the bulk power systems requires the use of electromagnetic transient (EMT) simulation tools to identify and mitigate system-wide stability risks. Conducting EMT studies for large-scale, IBR-rich grids, however, is challenging due to the inherent computational bottleneck caused by the underlying high-fidelity models and required small time steps. This paper introduces ParaEMT: an open-source, generic EMT simulation framework designed to accelerate simulations by leveraging advanced parallel computational technologies, such as high-performance computers. This paper presents a comprehensive exposition of ParaEMT, covering its modeling library, simulation strategy, framework structure, operational procedures, and auxiliary features, alongside its extensible parallel computational architecture. Notably, ParaEMT is a publicly accessible and modularized framework written in Python, thereby facilitating future development and the integration of new models and algorithms. The accuracy and efficiency of ParaEMT are demonstrated by rigorous validations via multiple case studies.

electromagnetic transient simulation↗
DOE OSTI2023

Identification of 2-Hydroxyacyl-CoA Synthases with High Acyloin Condensation Activity for Orthogonal One-Carbon Bioconversion

One-carbon (C1) compounds are emerging as cost-effective and potentially carbon-negative feedstocks for biomanufacturing, which require efficient, versatile metabolic platforms for the synthesis of value-added products. Synthetic formyl-CoA elongation (FORCE) pathways allow diverse product synthesis from C1 compounds via iterative C1 elongation, operating independently from the host metabolism with reduced engineering complexity and improved theoretical yields. However, a major bottleneck was identified as the suboptimal kinetics of the core C1–C1 condensation enzyme, 2-hydroxyacyl-CoA synthase (HACS), catalyzing the acyloin condensation reaction between formaldehyde and formyl-CoA. Furthermore, we used a combinatorial approach of bioprospecting and rational protein engineering to identify multiple HACS variants with significantly improved activities toward C1 substrates. Sequence and structure alignment of the active variants elucidated the key regions for the catalytic function, which were targeted for mutagenesis, leading to improved catalytic efficiency. In parallel, a consecutive round of bioprospecting for homologs with high similarity with active variants revealed a highly active HACS variant exhibiting up to 7-fold improvement in catalytic efficiency (k cat /K M ) and 14-fold improvement in the FORCE pathway flux in vivo compared to the previous reports. Upon further optimization of the downstream pathway, the orthogonal C1-to-product bioconversion system showed a metabolic flux of up to 700 μM glycolate OD –1 h –1 (2.1 mmol gDCW –1 h –1 ) and an industrially relevant glycolate titer, rate, and yield of 5.2 g L –1 (67.8 mM), 0.22 g L –1 h –1 , and 94% carbon yield, respectively.

2-hydroxyacyl-CoA synthase↗
DOE OSTI2022

Multi-process tooling for discontinuous carbon and hybrid glass fiber thermoplastics

Expensive tooling often constraints the use of composites in the design and development of automotive parts. While there is significant confidence and knowledge in sheet and bulk metals, composite processes are less understood in mass production environment. The processes used to produce composites and resulting properties are influenced by fiber length attrition, resin to fiber ratio, process waste etc. Tool designs are determined very early in the engineering process. It is cost prohibitive to build additional tools, in the event it becomes obvious a better processing method and material would be beneficial, the original decision is not easily changed. In the present work we recognize the bottleneck of tooling costs and provide an approach of multi-process tooling. The innovation of this work is the design and demonstration of a single tool for different processes namely injection, injection-compression and extrusion-compression. The materials used in this study were long and short fiber thermoplastics (LFTs and SFTs). The resulting structure-property relationships have been reported for the materials and processing methods with a battery tray (BT) tool.

Vaidya, Uday↗
DOE OSTI2023

An Open-Source Parallel EMT Simulation Framework: Preprint

As the integration level of inverter-based resources (IBR) increases, ensuring the reliable operation of the bulk power systems requires the use of electromagnetic transient (EMT) simulation tools to identify and mitigate system-wide stability risks. Conducting EMT studies for large-scale, IBR-rich grids, however, is challenging due to the inherent computational bottleneck caused by the underlying high-fidelity models and required small time steps. This paper introduces ParaEMT: an open-source, generic EMT simulation framework designed to accelerate simulations by leveraging advanced parallel computational technologies, such as high-performance computers. This paper presents a comprehensive exposition of ParaEMT, covering its modeling library, simulation strategy, framework structure, operational procedures, and auxiliary features, alongside its extensible parallel computational architecture. Notably, ParaEMT is a publicly accessible and modularized framework written in Python, thereby facilitating future development and the integration of new models and algorithms. The accuracy and efficiency of ParaEMT are demonstrated by rigorous validations via multiple case studies.

electromagnetic transient simulation↗
DOE OSTI2021

Combination of deep neural network with attention mechanism enhances the explainability of protein contact prediction

Abstract Deep learning has emerged as a revolutionary technology for protein residue‐residue contact prediction since the 2012 CASP10 competition. Considerable advancements in the predictive power of the deep learning‐based contact predictions have been achieved since then. However, little effort has been put into interpreting the black‐box deep learning methods. Algorithms that can interpret the relationship between predicted contact maps and the internal mechanism of the deep learning architectures are needed to explore the essential components of contact inference and improve their explainability. In this study, we present an attention‐based convolutional neural network for protein contact prediction, which consists of two attention mechanism‐based modules: sequence attention and regional attention. Our benchmark results on the CASP13 free‐modeling targets demonstrate that the two attention modules added on top of existing typical deep learning models exhibit a complementary effect that contributes to prediction improvements. More importantly, the inclusion of the attention mechanism provides interpretable patterns that contain useful insights into the key fold‐determining residues in proteins. We expect the attention‐based model can provide a reliable and practically interpretable technique that helps break the current bottlenecks in explaining deep neural networks for contact prediction. The source code of our method is available at https://github.com/jianlin-cheng/InterpretContactMap .

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Machine learning inversion of interatomic force constants from single-crystal inelastic neutron scattering

Atomic vibrations govern many macroscopic properties of materials, but experiments to comprehensively probe them remain challenging. Inelastic neutron scattering (INS) is a powerful technique to map phonon dispersions in crystals, especially when leveraging modern time-of-flight (ToF) spectrometers with large detectors. However, efficiently and robustly extracting interatomic force constants (FCs) parameterizing phonon dynamics from experimental spectra remains a bottleneck due to the complexity and high dimensionality of ToF INS datasets. Here, we present a machine learning approach for the direct inversion of FCs from single-crystal INS measurements. The framework leverages synthetic training data generated using universal machine-learned force fields and an efficient physics-based forward model. We benchmark two neural architectures–one emphasizing structured latent representation learning and the other direct, supervised spectral regression–across simulated datasets for two materials under idealized and noisy conditions. The latent-representation model is subsequently applied to experimental single-crystal INS data on germanium. The model is shown to reproduce FCs derived from both first-principles simulations and from iterative optimization, and furthermore achieves reliable inference even from sparse, single-orientation measurements representing short data acquisitions. Analysis of the learned latent space reveals semantically continuous and physically interpretable encodings that support strong cross-domain generalization. By bridging theoretical and experimental domains, we establish a path toward rapid inversion of experimental spectra and data-driven interpretation of temperature-dependent lattice dynamics.

42 ENGINEERING↗
DOE OSTI2024

Acid Etching‐Driven Self‐Assembly of Mn‐Shell Inducing Rock‐Salt Phase for Enhanced Single‐Crystal Ni‐Rich Cathodes

With the wide adoption of Li‐ion batteries, Ni‐rich cathode is considered as one of the most promising candidates of cathodes due to its high energy density and low cost. However, stability decreased with increasing Ni content in the Ni‐rich cathode. To solve this bottleneck, many strategies, such as coating, doping, surface modification, and special morphologies, have been developed. Herein, we introduce a groundbreaking approach for enhancing Ni‐rich cathode through an innovative acid etching process that promotes Mn shell self‐assembly, inducing a rock‐salt phase on the surface. This method not only simplifies the Ni‐rich cathode modification process, but also significantly improves the structural stability and electrochemical performance of Ni‐rich cathode. Our findings demonstrate that developed single‐crystal Ni‐rich cathode shows 3–34 % better stability compared to both commercial modified Ni‐rich cathode and unmodified counterparts. The unique Mn shell effectively mitigates reversible phase shifts during cycling, contributing to a remarkable enhancement in cycling stability. Additionally, this novel fabrication technique paves the way for cost‐effective production of high‐performance cathode materials, offering substantial benefits for lithium‐ion battery technology. And this study proves the potential of this method in advancing the design and development of durable, high‐capacity cathode materials for next‐generation batteries.

Ni-rich cathode↗
DOE OSTI2026

Challenges and Opportunities in the Enzymatic Recycling of Nylons

Enzymatic depolymerization has the potential to contribute to nylon waste recycling. However, the implementation of a viable industrial process still lags far behind progress in enzymatic polyester recycling. Here we review the current biocatalytic nylon recycling landscape, highlighting biochemical, structural, and materials science barriers that currently limit depolymerization extents. We detail efforts to identify, engineer, and characterize new nylon depolymerases, where currently even the best biocatalysts rarely exceed ~1 wt% conversion of solid polymer to products. Based on our analyses, we suggest that limited substrate accessibility due to substrate crystallinity and hydrogen bonding, rather than intrinsic enzyme inefficiency or instability, is the primary bottleneck to enhanced depolymerization. Guided by successes in polyester enzymatic recycling, we outline a research roadmap combining nylonase engineering with polymer pretreatment, chemo-enzymatic cascades, and process analyses to accelerate development of viable enzymatic nylon recycling processes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗