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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 OSTI2021

Invited: Bambu: an Open-Source Research Framework for the High-Level Synthesis of Complex Applications

This paper presents the open-source High-Level Synthesis research framework Bambu. The framework provides an open-source starting point to experiment with new ideas across High-Level Synthesis, high-level verification and debugging, FPGA/ASIC design, design flow space exploration, and parallel hardware accelerator design. The tool accepts as input standard C/C++ specifications and compiler intermediate representations (IRs) coming from the well-known Clang/LLVM and GCC com- pilers. The broad spectrum and flexibility of input formats allow the electronic design automation (EDA) research community to explore and integrate new transformations and optimizations. The easily extendable modular framework already includes many op- timizations and HLS benchmarks. The integration with synthesis and verification backends (commercial and open-source) allows researchers to quickly test any new finding and easily obtain performance and resource usage metrics for a given application. Different FPGA devices are supported from several different vendors: AMD/XILINX, Intel/Altera, Lattice Semiconductor, and NanoXplore. Finally, integration with the OpenRoad open-source end-to-end silicon compiler perfectly fits with the recent push towards open-source EDA.

Ferrandi, Fabrizio↗
DOE OSTI2026

HARMONY: Large-Scale Architecture Search for Efficient Hybrid Language Models

As large language models scale to trillions of parameters, their computational and memory requirements present critical challenges for efficient training and deployment. While Mixture of Experts (MoE) architectures enable efficient scaling through sparse parameter activation, and state-space models like Mamba offer linear-time complexity, principled methods for combining these paradigms remain undeveloped. We introduce HARMONY (Hybrid Architecture Research for Mamba, Optimized with Neural efficiencY), a multi-objective evolutionary neural architecture search framework for discovering efficient hybrid language models that integrate Transformer attention mechanisms, Mixture-of-Experts routing, and Mamba state-space components. Through large-scale distributed search using 16,384 MI250X GPUs on the Frontier supercomputer, HARMONY explores a comprehensive design space encompassing six attention variants (MHA, MQA, GQA, MLA, SWA, and Mamba-2), variable MoE configurations with both routed and shared experts, and extensive Mamba hyperparameters. Our framework discovers heterogeneous architectures that balance training performance with computational efficiency through multi-objective optimization incorporating latency penalties and fitness-based selection. Analysis of discovered architectures reveals that optimal hybrid designs favor heterogeneous component mixing rather than homogeneous patterns, with Mamba-2 and Multi-Head Latent Attention (MLA) emerging as preferred mechanisms. Discovered architectures demonstrate superior training efficiency: our best configuration achieves a final perplexity of 1.0874 with 2.38B parameters while processing 4,320 tokens/second, outperforming significantly larger manually designed models. Full-scale evaluation shows HARMONY's top architectures achieve better loss trajectories than equivalently-sized models using state-of-the-art configurations including Mixtral, Jamba, and Samba. Additionally, we demonstrate 91% weak scaling efficiency when training discovered 36B-parameter models across 1,024 GPUs. HARMONY is released as an open framework with comprehensive tools for building and training hybrid models using expert-data-pipeline parallelism, democratizing access to automated architecture design for next-generation language models.

Herron, Emily [ORNL] (ORCID:0000000273008172)↗
DOE OSTI2023

Structure and Electrochemical Properties of Bronze Phase Materials Containing Two Transition Metals

Bronze phase transition-metal oxides have recently attracted attention as high-rate lithium-ion battery anode materials. Their crystal structures are distinguished by large tunnels and an open framework, facilitating lithium-ion diffusion and high-rate charge–discharge properties. The presence of two transition metals also offers a route to achieve high energy density from multielectron redox. In this paper, we report the chemistry, structure, and electrochemical properties of two different bronze phase compositions having the same stoichiometry: W 3 Nb 2 O 14 and Mo 3 Nb 2 O 14 . These materials provide insight into how the transition metals affect the electrochemical behavior and structural stability of bronze phase materials. Mo 3 Nb 2 O 14 exhibits greater than 1 electron redox per transition metal leading to lithium capacities above 200 mAh g –1 at C/2 but is unable to maintain this high capacity at high rates due to incomplete Mo redox reactions. In contrast, W 3 Nb 2 O14 exhibits reversible redox reactions and retains its open structure on cycling. This study highlights the potential of bronze phase materials containing two transition metals to exhibit fast charging properties with a high energy density.

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

Bismuth-Polyoxocation Coordination Networks: Controlling Nuclearity and Dimension-Dependent Photocatalysis

Bismuth-oxocluster nodes for metal–organic frameworks (MOFs) and coordination networks/polymers are less prolific than other families featuring zinc, zirconium, titanium, lanthanides, etc. However, Bi 3+ is non-toxic, it readily forms polyoxocations, and its oxides are exploited in photocatalysis. This family of compounds provides opportunity in medicinal and energy applications. Here, we show that Bi node nuclearity depends on solvent polarity, leading to a family of Bi x -sulfonate/carboxylate coordination networks with x = 1–38. Larger nuclearity-node networks were obtained from polar and strongly coordinating solvents, and we attribute the solvent’s ability to stabilize larger species in solution. Here, the strong role of the solvent and the lesser role of the linker in defining node topologies differ from other MOF syntheses, and this is due to the Bi 3+ intrinsic lone pair that leads to weak node–linker interactions. We describe this family by single-crystal X-ray diffraction (eleven structures), obtained in pure forms and high yields. Ditopic linkers include NDS (1,5-naphthalenedisulfonate), DDBS (2,2'-[biphenyl-4,4'-diylchethane-2,1-diyl] dibenzenesulphonate), and NH 2 -benzendicarboxylate (BDC). While the BDC and NDS linkers yield more open-framework topologies that resemble those obtained by carboxylate linkers, topologies with DDBS linkers appear to be in part driven by association between DDBS molecules. An in situ small-angle X-ray scattering study of Bi 38 -DDBS reveals stepwise formation, including Bi 38 -assembly, pre-organization in solution, followed by crystallization, confirming the less important role of the linker. We demonstrate photocatalytic hydrogen (H 2 ) generation with select members of the synthesized materials without the benefit of a co-catalyst. Band gap determination from X-ray photoelectron spectroscopy (XPS) and UV–vis data suggest the DDBS linker effectively absorbs in the visible range with ligand-to-Bi-node charge transfer. In addition, materials containing more Bi (larger Bi 38 -nodes or Bi 6 inorganic chains) exhibit strong UV absorption, also contributing to effective photocatalysis by a different mechanism. All tested materials became black with extensive UV–vis exposure, and XPS, transmission electron microscopy, and X-ray scattering of the black Bi 38 -framework suggest that Bi o is formed in situ, without phase segregation. This evolution leads to enhanced photocatalytic performance, perhaps due to increased light absorption.

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

Universal Polarization Transformations: Spatial Programming of Polarization Scattering Matrices Using a Deep Learning‐Designed Diffractive Polarization Transformer

Abstract Controlled synthesis of optical fields having nonuniform polarization distributions presents a challenging task. Here, a universal polarization transformer is demonstrated that can synthesize a large set of arbitrarily‐selected, complex‐valued polarization scattering matrices between the polarization states at different positions within its input and output field‐of‐views (FOVs). This framework comprises 2D arrays of linear polarizers positioned between isotropic diffractive layers, each containing tens of thousands of diffractive features with optimizable transmission coefficients. After its deep learning‐based training, this diffractive polarization transformer can successfully implement N i N o = 10 000 different spatially‐encoded polarization scattering matrices with negligible error, where N i and N o represent the number of pixels in the input and output FOVs, respectively. This universal polarization transformation framework is experimentally validated in the terahertz spectrum by fabricating wire‐grid polarizers and integrating them with 3D‐printed diffractive layers to form a physical polarization transformer. Through this set‐up, an all‐optical polarization permutation operation of spatially‐varying polarization fields is demonstrated, and distinct spatially‐encoded polarization scattering matrices are simultaneously implemented between the input and output FOVs of a compact diffractive processor. This framework opens up new avenues for developing novel devices for universal polarization control and may find applications in, e.g., remote sensing, medical imaging, security, material inspection, and machine vision.

Optical neural networks↗
DOE OSTI2022

What Is the Smallest Zeolite That Could Be Synthesized?**

Abstract Zeolites with a few unit cells are promising as catalyst and adsorbents. The quest to synthesize the smallest zeolites has recently resulted in 4 to 8 nm nanozeolites, about 2 to 4 unit cells. These findings pose the question of what is the smallest zeolite that could be obtained by hydrothermal synthesis. Here we address this question using molecular simulations and thermodynamic analysis. The simulations predict that amorphous precursors as small as 4 nm can crystallize zeolites, in agreement with the experiments. We find that interfacial forces dominate the structure of smaller particles, resulting in size‐dependent compact isomers that have ring and pore distributions different from open framework zeolites. The instability of zeolites smaller than 3±0.5 nm precludes a classical mechanism of nucleation from solution or through assembly of small nanoslabs.

Dhabal, Debdas↗
DOE OSTI2022

What Is the Smallest Zeolite That Could Be Synthesized?**

Zeolites with a few unit cells are promising as catalyst and adsorbents. The quest to synthesize the smallest zeolites has recently resulted in 4 to 8 nm nanozeolites, about 2 to 4 unit cells. These findings pose the question of what is the smallest zeolite that could be obtained by hydrothermal synthesis. We address this question using molecular simulations and thermodynamic analysis. The simulations predict that amorphous precursors as small as 4 nm can crystallize zeolites, in agreement with the experiments. We find that interfacial forces dominate the structure of smaller particles, resulting in size-dependent compact isomers that have ring and pore distributions different from open framework zeolites. The instability of zeolites smaller than 3±0.5 nm precludes a classical mechanism of nucleation from solution or through assembly of small nanoslabs.

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

Na 4 Fe 1.5 Mn 1.5 (PO 4 ) 2 (P 2 O 7 ): A low-cost and earth-abundant cathode for robust sodium storage

The mixed compounds of phosphates and pyrophosphates are attractive cathodes for sodium-ion batteries (SIBs) owing to their robust open framework structure and superior diffusion dynamics. However, most reported mixed phosphate cathodes generally suffer from low operating potential. Herein, we develop a bimetallic Na 4 Fe 1.5 Mn 1.5 (PO 4 ) 2 (P 2 O 7 )/C-rGO (NFMPP/C-rGO) cathode, which possesses two working plateaus at 2.92 and 3.95 V. The obtained NFMPP/C-rGO demonstrates a stable high capacity of over 120 mAh g −1 at 0.1 C. In-situ XRD characterization discloses a solid solution reaction for the Fe 2+/3+ redox couple and a two-phase reaction for the Mn 2+/3+ redox couple. In conclusion, first-principles calculations reveal the migration of Na + in NFMPP has low barriers. This work provides a new, low-cost, earth-abundant, and stable cathode choice for practical SIBs.

Cathode materials↗
DOE OSTI2025

Understanding and Tuning the Electronic Properties of Prussian Blue Analogues

Prussian blue analogues (PBAs) have attracted increasing interest owing to their potential applications in various fields such as energy storage and conversion, neuromorphic computing, and magnetic switching. With a general formula of A x M N [M C (CN) 6 ], they feature an open framework that provides abundant channels for diffusion of alkali metal ions A and allows flexible compositional control of transition metal ions M N and M C . The oxidation states of transition metal ions can be tuned by adjusting the amount (x) of alkali ions A. Here, we carried out density functional theory calculations combined with experimental measurements to investigate the effects of transition metal ions, alkali ions, and oxidation states on the electronic properties of PBAs. Our calculations found that the band gaps of PBAs can be tuned from close to 0 eV to more than 4 eV. Experimentally, we introduced the synthesis/characterization of five previously unreported PBAs (M N = Ru, Os; M C = Fe, Ru, and Os) to complete the nine stable M N :M C transition metal combinations in group VIII of the periodic table. The optically measured intervalence charge transfer excitation energies of group VIII PBAs are consistent with calculated band gaps. They demonstrate wide band gap tunability by adjusting transition metals and oxidation states, enabling semiconductor-to-metal transitions for memristor applications and enhancing electronic conductivity for battery applications. In conclusion, this work provides a computational/experimental database of electronic properties versus structural compositions for PBAs.

Electrical conductivity↗
DOE OSTI2025

Single Crystals of Vanadium Oxides as a Lens for Understanding Structural and Electronic Phase Transformations, Ion Transport, Chemo-Mechanical Coupling, and Electrothermal Neuronal Emulation

Vanadium oxides cystallize in a diverse array of structures and compositions arising from the redox versatility of vanadium, variable covalency of V−O bonds, and myriad coordination geometries. Their open frameworks present abundant interstitial sites that enable insertion of guest-ions. In such compounds, V3d electron and spin localization and disorder couple strongly to structural preferences. The rich structural diversity manifests as a “rugged” free energy landscape with multiple interconvertible polymorphs. Such a landscape sets up structural, electronic, and magnetic transitions that underpin the promise of these materials as ion-insertion battery electrodes; compact primitives for brain-inspired computing, and heterogeneous catalysts. Here, we examine the structural and compositional diversity, electronic instabilities, defect dynamics, structure transformations, mechanical properties, and surface structure of vanadium oxides using single crystals as a distinctive lens. Single crystals enable the measurement of structure−function correlations without the ensemble and orientational averaging inevitable in polycrystalline materials. Their well-defined surfaces further enable examination of facet-dependent reactivity toward molecular adsorbates, ion fluxes, and lattice (mis)matched solids. We provide a comprehensive account of vanadium-oxide single-crystal studies, from delineation of common structural motifs to single-crystal growth techniques, topochemical modification strategies, mechanisms underpinning electronic instabilities, and implementation as electrothermal neurons and battery electrode materials.

Ponis, John [Texas A&M University, College Station↗
DOE OSTI2025

Electronic Structure and Safety Insights into Prussian Blue Analog Cathode Behavior at Elevated Temperatures in Sodium-Ion Batteries

Prussian blue analogs (PBAs) represent promising cathode materials for sodium-ion batteries (SIBs) due to their high theoretical capacity, open framework structure, and use of earth-abundant elements. However, the high-temperature structural evolution, water content effects, and thermal safety of PBAs, particularly in charged states, remain poorly understood, hindering their practical deployment. Here, we investigate Na 2 Fe[Fe(CN) 6 ]·2H 2 O using thermogravimetric analysis (TGA), ex situ and in situ temperature-dependent X-ray absorption spectroscopy (XAS), and accelerated rate calorimetry (ARC). TGA and ex situ XAS confirm water loss between 150 and 200 °C, resulting in Fe 2+ oxidation, enhanced local symmetry, and uniform redox behavior that improves electrochemical performance. In situ XAS reveals irreversible structural changes above 240 °C, including ligand loss, Fe site distortion, and increased disorder, while ARC on charged electrodes shows minimal self-heating rates (<0.1 °C/min) up to 300 °C, indicating exceptional thermal stability without lattice oxygen release. These insights elucidate PBA thermal dynamics, demonstrating improved electrochemical performance of water-deficient PBAs and informing future material design and safety assessment for SIB applications.

batteries↗
DOE OSTI2022

Microbial and Environmental Processes Shape the Link between Organic Matter Functional Traits and Composition

Dissolved organic matter (DOM) is a large and complex mixture of molecules that fuels biogeochemical reaction in virtually all ecosystems on Earth. However, the relative importance of deterministic and stochastic processes in structuring DOM composition remains poorly characterized. Here we develop a framework for partitioning molecular composition based on key molecular traits, including lability vs. recalcitrance and activity vs. inactivity. Within this framework, we examine the ecological processes governing the assembly of DOM fractions by deploying aquatic microcosms on mountainsides that span gradients of temperature and nutrient loading in subtropical and subarctic ecosystems. Across study regions, deterministic and stochastic processes primarily structure active and inactive fractions, respectively. However, recalcitrant molecules are more deterministically assembled than labile molecules in the inactive fraction. Deterministic processes leading to variable selection generally exhibit more variation across the energy supply gradient for inactive fractions, and their importance increases with energy supply for recalcitrant molecules in both active and inactive fractions. Together, our results indicate that active and inactive fractions of DOM assemblages are structured by contrasting ecological processes, and their recalcitrant components are sensitive to global change. In conclusion, our framework opens new avenues to understand the assembly and turnover of DOM in a changing world, which can be used to predict carbon cycling at local to global scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Accelerating phase-field-based microstructure evolution predictions via surrogate models trained by machine learning methods

Abstract The phase-field method is a powerful and versatile computational approach for modeling the evolution of microstructures and associated properties for a wide variety of physical, chemical, and biological systems. However, existing high-fidelity phase-field models are inherently computationally expensive, requiring high-performance computing resources and sophisticated numerical integration schemes to achieve a useful degree of accuracy. In this paper, we present a computationally inexpensive, accurate, data-driven surrogate model that directly learns the microstructural evolution of targeted systems by combining phase-field and history-dependent machine-learning techniques. We integrate a statistically representative, low-dimensional description of the microstructure, obtained directly from phase-field simulations, with either a time-series multivariate adaptive regression splines autoregressive algorithm or a long short-term memory neural network. The neural-network-trained surrogate model shows the best performance and accurately predicts the nonlinear microstructure evolution of a two-phase mixture during spinodal decomposition in seconds, without the need for “on-the-fly” solutions of the phase-field equations of motion. We also show that the predictions from our machine-learned surrogate model can be fed directly as an input into a classical high-fidelity phase-field model in order to accelerate the high-fidelity phase-field simulations by leaping in time. Such machine-learned phase-field framework opens a promising path forward to use accelerated phase-field simulations for discovering, understanding, and predicting processing–microstructure–performance relationships.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Emergence of the Chern Supermetal and Pair-Density Wave through Higher-Order Van Hove Singularities in the Haldane-Hubbard Model

While advances in electronic band theory have brought to light new topological systems, understanding the interplay of band topology and electronic interactions remains a frontier question. In this work, we predict new interacting electronic orders emerging near higher-order Van Hove singularities present in the Chern bands of the Haldane model. We classify the nature of such singularities and employ unbiased renormalization group methods that unveil a complex landscape of electronic orders, which include ferromagnetism, density waves, and superconductivity. Importantly, we show that repulsive interactions can stabilize the long-sought pair-density-wave state and an exotic Chern supermetal, which is a new class of non-Fermi liquid with anomalous quantum Hall response. Further, this framework opens a new path to explore unconventional electronic phases in two-dimensional chiral bands through the interplay of band topology and higher-order Van Hove singularities.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

ChatGrid: Power Grid Visualization Empowered by Large Language Model

This paper presents a novel open framework, ChatGrid, for easy, intuitive, and interactive geospatial visualization of large-scale transmission networks. ChatGrid uses state-of-the-art techniques for geospatial visualization of large networks including 2.5D views, animated flows, hierarchical and level-based filtering, and aggregation to provide visual information in an easy, cognitive manner. The highlight of ChatGrid is a natural language query based interface powered by large language model (ChatGPT) that offers an enhanced interactive experience whereby the user asks a question and ChatGrid provides the information both in text and visual. We discuss the architecture, implementation, design decisions, and usage of large language model for ChatGrid.

Jin, Sichen↗
DOE OSTI2021

Accelerating Finite-Temperature Kohn-Sham Density Functional Theory with Deep Neural Networks

We present a numerical modeling workflow based on machine learning (ML) which reproduces the total energies produced by Kohn-Sham density functional theory (DFT) at finite electronic temperature to within chemical accuracy at negligible computational cost. Based on deep neural networks, our workflow yields the local density of states (LDOS) for a given atomic configuration. From the LDOS, spatially-resolved, energy-resolved, and integrated quantities can be calculated, including the DFT total free energy, which serves as the Born-Oppenheimer potential energy surface for the atoms. We demonstrate the efficacy of this approach for both solid and liquid metals and compare results between independent and unified machine-learning models for solid and liquid aluminum. Our machine-learning density functional theory framework opens up the path towards multiscale materials modeling for matter under ambient and extreme conditions at a computational scale and cost that is unattainable with current algorithms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Proton Tunable Analog Transistor for Low Power Computing

This project was broadly motivated by the need for new hardware that can process information such as images and sounds right at the point of where the information is sensed (e.g. edge computing). The project was further motivated by recent discoveries by group demonstrating that while certain organic polymer blends can be used to fabricate elements of such hardware, the need to mix ionic and electronic conducting phases imposed limits on performance, dimensional scalability and the degree of fundamental understanding of how such devices operated. As an alternative to blended polymers containing distinct ionic and electronic conducting phases, in this LDRD project we have discovered that a family of mixed valence coordination compounds called Prussian blue analogue (PBAs), with an open framework structure and ability to conduct both ionic and electronic charge, can be used for inkjet-printed flexible artificial synapses that reversibly switch conductance by more than four orders of magnitude based on electrochemically tunable oxidation state. Retention of programmed states is improved by nearly two orders of magnitude compared to the extensively studied organic polymers, thus enabling in-memory compute and avoiding energy costly off-chip access during training. We demonstrate dopamine detection using PBA synapses and biocompatibility with living neurons, evoking prospective application for brain - computer interfacing. By application of electron transfer theory to in-situ spectroscopic probing of intervalence charge transfer, we elucidate a switching mechanism whereby the degree of mixed valency between N-coordinated Ru sites controls the carrier concentration and mobility, as supported by density functional theory (DFT) .

97 MATHEMATICS AND COMPUTING↗