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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

Automatic Differentiation in MetaPhysicL and Its Applications in MOOSE

Efficient solution via Newton’s method of nonlinear systems of equations requires an accurate representation of the Jacobian, corresponding to the derivatives of the component residual equations with respect to the degrees of freedom. In practice these systems of equations often arise from spatial discretization of partial differential equations used to model physical phenomena. These equations may involve domain motion or material equations that are complex functions of the systems’ degrees of freedom. Computing the Jacobian by hand in these situations is arduous and prone to error. Finite difference approximations of the Jacobian or its action are prone to truncation error, especially in multiphysics settings. Symbolic differentiation packages may be used, but often result in an excessive number of terms in realistic model scenarios. An alternative to symbolic and numerical differentiation is automatic differentiation (AD), which propagates derivatives with every elementary operation of a computer program, corresponding to continual application of the chain rule. Automatic differentiation offers the guarantee of an exact Jacobian at a relatively small overhead cost. In this work, we outline the adoption of AD in the Multiphysics Object Oriented Simulation Environment (MOOSE) via the MetaPhysicL package. We describe the application of MOOSE’s AD capability to several sets of physics that were previously infeasible to model via hand-coded or Jacobian-free simulation techniques, including arbitrary Lagrangian-Eulerian and level-set simulations of laser melt pools, phase-field simulations with free energies provided through neural networks, and metallic nuclear fuel simulations that require inner Newton loop calculation of nonlinear material properties.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Refining fast calorimeter simulations with a Schrödinger Bridge

Machine learning-based simulations, especially calorimeter simulations, are promising tools for approximating the precision of classical high energy physics simulations with a fraction of the generation time. Nearly all methods proposed so far learn neural networks that map a random variable with a known probability density, like a Gaussian, to realistic-looking events. In many cases, physics events are not close to Gaussian and so these neural networks have to learn a highly complex function. We study an alternative approach: Schrödinger bridge Quality Improvement via Refinement of Existing Lightweight Simulations (SQuIRELS). SQuIRELS leverages the power of diffusion-based neural networks and Schrödinger bridges to map between samples where the probability density is not known explicitly. We apply SQuIRELS to the task of refining a classical fast simulation to approximate a full classical simulation. On simulated calorimeter events, we find that SQuIRELS is able to reproduce highly non-trivial features of the full simulation with a fraction of the generation time.

Calorimeter methods↗
DOE OSTI2026

Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning

This paper explores uncertainty quantification (UQ) methods in the context of Kolmogorov–Arnold Networks (KANs). We apply an ensemble approach to KANs to obtain a heuristic measure of UQ, enhancing interpretability and robustness in modeling complex functions. Building on this, we introduce Conformalized-KANs, which integrate conformal prediction, a distribution-free UQ technique, with KAN ensembles to generate calibrated prediction intervals with guaranteed coverage.} Extensive numerical experiments are conducted to evaluate the effectiveness of these methods, focusing particularly on the robustness and accuracy of the prediction intervals under various hyperparameter settings. We show that the conformal KAN predictions can be applied to recent extensions of KANs, including Finite Basis KANs (FBKANs) and multifideilty KANs (MFKANs). The results demonstrate the potential of our approaches to significantly improve the reliability and applicability of KANs in scientific machine learning.

• Artificial intelligence (AI) / machine learning ↗
DOE OSTI2023

Breaking Rayleigh’s Law with Spatially Correlated Disorder to Control Phonon Transport

Controlling thermal transport in insulators and semiconductors is crucial for many technological fields such as thermoelectrics and thermal insulation, for which a low thermal conductivity (κ) is desirable. A major obstacle for realizing low κ materials is Rayleigh’s law, which implies that acoustic phonons, which carry most of the heat, are insensitive to scattering by point defects at low energy. We demonstrate, with large scale simulations on tens of millions of atoms, that isotropic long-range spatial correlations in the defect distribution can dramatically reduce phonon lifetimes of important low-frequency heat-carrying modes, leading to a large reduction of κ—potentially an order of magnitude at room temperature. We propose a general and quantitative framework for controlling thermal transport in complex functional materials through structural spatial correlations, and we establish the optimal functional form of spatial correlations that minimize κ. Finally, we end by briefly discussing experimental realizations of various correlated structures.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Design and Challenges of Edge Computing ASICs on Front-End Electronics

In situ or hardware-embedded data processing of raw signals, close to their source, in radiation detectors is expected to provide dramatic improvements in data quality and volumes. However, the implementation of artificial neural networks (ANNs) in the front-end electronics, and the design of custom integrated circuits (ASICs), comes with challenges. In addition, detectors have to operate with limited power budget and implement complex functionalities in a very dense space. They often are exposed to extreme conditions as they work in high-radiation environments and/or cryogenic temperatures. This paper presents examples of applications and design methodologies for in-situ ANNs, along with the challenges of retaining the fidelity of the trained networks. For illustration, we use the problem of estimating the energy deposited by the radiation from digitized waveforms. The proposed implementation starts with an ML algorithm trained in Qkeras and eventually leads to an equivalent ASIC implementation. The associated design challenges in realizing energy and area efficient implementations in CMOS processes are reviewed. Novel approaches that employ hybrid technologies (combination of CMOS with memristors), in-memory computing models, and bio-inspired spiking neural networks are also highlighted

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Utilisation of flavonoids from Lycium barbarum L. leaves cross‐linked with whey protein for the stabilisation and delivery of β ‐carotene emulsions

Summary Although β ‐carotene is an essential nutrient for the maintenance of human health, it is insoluble in water and extremely unstable, which limits its application. Here, we prepared a stable emulsification system of whey protein (WP)‐ Lycium barbarum L. leaves flavonoids (LBLF) complex as a carrier to load β ‐carotene. The rheological properties, antioxidant properties and stability of emulsion were studied, and the effects of emulsion on the biological retention of β ‐carotene and stability were investigated. The results indicate that, compared with WP, the WP‐LBLF emulsion has smaller particles, a smaller creaming index, and significantly enhanced chemical stability under light and heat treatment conditions. The thiobarbituric acid value of the WP‐LBLF emulsion (0.031 ± 0.016 mg/kg) was dramatically lower than that of the WP emulsion (0.045 ± 0.012 mg/kg) ( P < 0.05). In the simulated gastrointestinal digestion experiment, the retention rate of β ‐carotene was higher in WP‐LBLF emulsion. Additionally, the WP‐LBLF emulsion loaded with β ‐carotene has good salt tolerance and heat stability. In our study, WP‐LBLF was used as an emulsifier to load β ‐carotene emulsion to improve its bioavailability and stability, which provides a new preparation and application method for a complex functional emulsifier.

Fan, Yanli↗
DOE OSTI2026

Multifunctional electrochemical memory stabilized by phase coexistence

Our growing computing needs, especially in applications that heavily rely on artificial intelligence (AI), motivate a search for new components that could substantially augment the performance of general-purpose digital computers. Beyond ON/OFF switching, new components with linear multistate analog resistive tuning, nonlinear volatile switching, spiking, oscillatory, stochastic and other complex functionalities could enable highly efficient neuromorphic computing schemes for AI information processing. Compared to the extreme multifunctionality of biological neurons, realizing all the above characteristics in a single, scalable analog component remains a grand challenge. Here we investigate electrochemical gating combined with localized thermal activation to program and switch a single, vertically integrated and dimensionally scaled electrothermal chemical random access memory (ETCRAM) with a channel and reservoir composed of phase-separated vanadium oxide. Closely related to electrochemical RAM (ECRAM), ETCRAM uses an integrated gate-heater electrode to overcome kinetic barriers that help retain states at ambient temperatures. In addition to synapse-like stable and programmable analog resistance states arising from redox-tunable phase coexistence, a single component exhibits neuron-like nonlinear conductance switching with a tunable threshold and self-driven dynamics owing to the thermally driven metal-insulator phase transition in vanadium dioxide. More broadly, we demonstrate that electrochemically stabilized phase coexistence could unlock analog electronics with novel functionality, stability, reconfigurability, and scalability.

Oh, Sangheon [Sandia National Lab. (SNL-CA), Liver↗
DOE OSTI2025

Nickel promotes selective ethylene epoxidation on silver

Over the last 80 years, chlorine (Cl) has been the primary promoter of the ethylene epoxidation reaction valued at ~40 billion USD per year, providing a ~25% selectivity increase over unpromoted silver (Ag) (~55%). Promoters such as cesium, rhenium, and molybdenum each add a few percent of selectivity enhancements to achieve 90% overall, but their codependence on Cl makes optimizing and understanding their function complex. Here, we took a theory-guided, single-atom alloy approach to identify nickel (Ni) as a dopant in Ag that can facilitate selective oxidation by activating molecular oxygen (O 2 ) without binding oxygen (O) too strongly. Surface science experiments confirmed the facile adsorption/desorption of O 2 on NiAg, as well as demonstrating that Ni serves to stabilize unselective nucleophilic oxygen. Supported Ag catalyst studies revealed that the addition of Ni in a 1:200 Ni to Ag atomic ratio provides a ~25% selectivity increase without the need for Cl co-flow and acts cooperatively with Cl, resulting in a further 10% initial increase in selectivity.

36 MATERIALS SCIENCE↗
DOE OSTI2024

SmartFuse: Reconfigurable Smart Switches to Accelerate Fused Collectives in HPC Applications

Communication switches have sometimes been augmented to process collectives (e.g., the IBM BlueGene project and the Mellanox SHArP switch). In this work, we find that there is a great acceleration opportunity through the further augmentation of switches to accelerate more complex functions that combine communication with computation. We consider three types of such functions. The first is fully-fused collectives built by fusing multiple existing collectives like Allreduce with Alltoall. The second is semi-fused collectives built by combining a collective with another computation. The third we refer to as higher-order collectives built by combining multiple computations and communications, such as to perform matrix-matrix multiply (PGEMM). In this work, we propose a framework called SmartFuse to accelerate fused collective functions. The core of SmartFuse is a reconfigurable smart switch to support these operations. The semi/fully fused collectives are implemented with a CGRAlike architecture, while higher-order collectives are implemented with a more specialized computational unit that can also schedule communication. Supporting our framework is software to evaluate and translate relevant parts of the input program, compile them into a control data flow graph, and then map this graph to the switch hardware. The proposed framework, once deployed, has the strong potential to accelerate existing HPC applications transparently by encapsulation within an MPI implementation. Experimental results show that this approach improves the performance of the PGEMM kernel, MINIFE, and AMG by, on average, 94%, 15%, and 13%, respectively.

Haghi, Pouya↗
DOE OSTI2020

rlmolecule: A library for general-purpose material and molecular optimization using AlphaZero-style reinforcement learning

This library includes in-progress code for the optimization of materials and molecules using reinforcement learning. This project seeks to develop a generalized machine learning approach for optimizing targeted, and often complex, functional properties over the space of possible material structures, allowing faster identification of promising candidates. Electronic and transport properties of materials are determined by their molecular structure (for molecules) or their crystal structure (for in-organic crystalline materials). The atomic composition, bonding, and 3-D-spatial arrangement in these systems therefore represent a vast, discontinuous search space for optimal candidates. This library implements AlphaZero style reinforcement learning applied to two worked examples: the optimization of stable organic radicals for redox flow batteries, and the determination of stable electrolyte species for solid state batteries. The current code contains proof-of-concept results run on the Eagle HPC system, while the completed package will be a flexible tool for material optimization across of number of energy-relevant fields, which leverages cloud compute resources for the majority of the computational heavy lifting.

Biagioni, David↗
DOE OSTI2022

Comparative metabolomic analysis reveals shared and unique chemical interactions in sponge holobionts

Sponges are ancient sessile metazoans, which form with their associated microbial symbionts a complex functional unit called a holobiont. Sponges are a rich source of chemical diversity; however, there is limited knowledge of which holobiont members produce certain metabolites and how they may contribute to chemical interactions. To address this issue, we applied non-targeted liquid chromatography tandem mass spectrometry (LC-MS/MS) and gas chromatography mass spectrometry (GC-MS) to either whole sponge tissue or fractionated microbial cells from six different, co-occurring sponge species. Several metabolites were commonly found or enriched in whole sponge tissue, supporting the notion that sponge cells produce them. These include 2-methylbutyryl-carnitine, hexanoyl-carnitine and various carbohydrates, which may be potential food sources for microorganisms, as well as the antagonistic compounds hymenialdisine and eicosatrienoic acid methyl ester. Metabolites that were mostly observed or enriched in microbial cells include the antioxidant didodecyl 3,3'-thiodipropionate, the antagonistic compounds docosatetraenoic acid, and immune-suppressor phenylethylamide. This suggests that these compounds are mainly produced by the microbial members in the sponge holobiont, and are potentially either involved in inter-microbial competitions or in defenses against intruding organisms. This study shows how different chemical functionality is compartmentalized between sponge hosts and their microbial symbionts and provides new insights into how chemical interactions underpin the function of sponge holobionts.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Metalens formed by structured arrays of atomic emitters

Abstract Arrays of atomic emitters have proven to be a promising platform to manipulate and engineer optical properties, due to their efficient cooperative response to near‐resonant light. Here, we theoretically investigate their use as an efficient metalens. We show that, by spatially tailoring the (subwavelength) lattice constants of three consecutive two‐dimensional arrays of identical atomic emitters, one can realize a large transmission coefficient with arbitrary position‐dependent phase shift, whose robustness against losses is enhanced by the collective response. To characterize the efficiency of this atomic metalens, we perform large‐scale numerical simulations involving a substantial number of atoms (N∼ 5 × 10 5 ) that is considerably larger than comparable works. Our results suggest that low‐loss, robust optical devices with complex functionalities, ranging from metasurfaces to computer‐generated holograms, could be potentially assembled from properly engineered arrays of atomic emitters.

Materials Science↗
DOE OSTI2020

Examination of actinide chemistry at solid-water interfaces to support advanced actinide separations (Final Report)

In this work we have examined the thermodynamics of actinide interactions with solid interfaces in the presence and absence of multi-functional complexing ligands. The term “interactions” is a global term to capture many potential reactions including aqueous complexation, sorption/surface complexation, surface-mediated redox reactions, electrostatic attraction and repulsion, surface passivation, surface precipitation, and counter-ion competition. Each of these potential reactions may control actinide interactions with a solid surface. This work is helping to develop a more fundamental understanding of actinide bonding at solid:water interfaces and examine unique surface mediated oxidation/reduction reactions which are not expected based on the reduction potentials of aqueous species. We place a large emphasis on understanding both sorption and desorption processes as a means of understanding the sorption mechanisms. This work is directly aligned with the DOE BES Heavy Element Chemistry programs interests in understanding “the chemical and physical properties of these elements to determine solution, interfacial and solid-state bonding and reactivity”.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2023

Bioinspired mineralizing microenvironments generated by liquid-liquid phase coexistence

Biominerals such as bones, teeth and shells exhibit improved mechanical properties and intricate morphologies not seen in nonbiologically-produced minerals of ostensibly the same composition. These remarkable properties of biogenic minerals are thought to arise due to precise local control over the mineral deposition process, including organic and inorganic inclusions. Understanding how Biology controls the local reaction environment during formation of these materials to control their composition, structure, and properties is a grand challenge that promises to enable new routes to high-performance materials. This project developed multi-compartment bioinspired microreactors as artificial mineralizing vesicles, and used them to understand and control formation of inorganic/organic composite solid materials. A major emphasis was on developing all-aqueous emulsions in which each droplet was a structured microreactor with two or more adjacent phases. This approach provided local control over reaction environment including availability of inclusions such as polypeptides and metal ions, while being sufficiently simple to produce large populations of essentially identical multiphase reactor droplets simultaneously within a batch. Organic/inorganic composite materials could be produced with very high organic content that stabilized the inorganic portions as amorphous materials (e.g., amorphous calcium carbonate) by preventing the typical conversion to more thermodynamically crystalline forms (e.g., calcite). These stabilized amorphous composites could be stored indefinitely and converted to crystalline forms later by removal of the organic inclusions via, for example, heating. Compositional gradients in the organic and inorganic components were embedded during synthesis due to the evolution of the reaction microenvironment, and despite the complexity of this process it occurred similarly across the population of reactive droplets and was hence encoded into the population of resulting composite particles. The approach developed here allows pre-structuring of reactive microenvironments to control the spatiotemporal reaction environment, which is an important step towards rational design and on-demand production of complex functional materials with desired composition, optical properties, and mechanical response.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Electronic Structure, Spectroscopy and Correlation Effects in Novel Materials

This project was targeted on electronic structure, spectroscopic studies, and correlation effects in a variety of novel materials of great current interest. Spectroscopies resolved highly in momentum, energy or spatial dimensions are playing a key role in unraveling the nature of the ground state and excitation properties in wide classes of novel materials. The seminal insights thus obtained are of critical importance not only for answering some of the fundamental questions facing condensed matter physics and materials science today, but also for understanding and thus helping to design and develop new materials with desirable properties, which will continue to be the key to the survival of mankind and its energy needs as a technological society long into the future. However, spectroscopies do not provide a direct map of electronic states, but act as a very complex ‘filter’ or ‘mapping’ of the underlying spectrum. This connection between the electronic states and measured spectra—called the ‘matrix element effect’—is in general an extremely complex function of the phase space of the experiment (e.g. energy/polarization of photons in photoemission), presenting both a challenge and an opportunity. So motivated, this project pursued techniques for realistic treatment of electronic spectra of a wide variety of materials, which served as a prelude to formulating and implementing methodologies for making direct connection with various spectroscopies such as ARPES, STS/STM, and inelastic light scattering. Specific systems are topological materials, including 2D ultrathin films beyond graphene, novel superconductors, and nanocrystals, among others. Our goal was to exploit the strengths of various spectroscopies to piece together the most complete picture of electronic states in systems of current interest, enabling direct and sharpened confrontations with theoretical models, and also to help advance the reach of various spectroscopies.

36 MATERIALS SCIENCE↗
DOE OSTI2024

PhILMs: Collaboratory on Mathematics and Physics-Informed Learning Machines for Multiscale and Multiphysics Problems

The landscape of computational science and engineering is continually evolving, with the challenge of high-dimensional regression problems standing as a significant hurdle in numerous scientific endeavors. Addressing this challenge, our research, funded by this award, has led to the development of an innovative computational framework known as Probabilistic Partition of Unity Networks (PPOU-Nets). This initiative represents a collaborative effort to harness the potential of mathematics and physics-informed machine learning in tackling multiscale and multiphysics problems prevalent in high-dimensional spaces. Through this work, we have proposed a novel methodology that seamlessly integrates adaptive dimensionality reduction and a mixture of experts model, thereby facilitating a more efficient and accurate approximation of complex functions. This research effort has not only advanced the state of computational science but also opened new avenues for exploration in quantum computing and beyond. This report outlines the motivation, methodology, key findings, and implications of our work, underscoring our contributions to the broader scientific community and the potential pathways for future research.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Understanding the Thermal Physics and Metallurgy of Metal Big Area Additive Manufacturing

The research goal of this EPSCoR-DOE partnership is to mitigate defects in parts made using a new type of additive manufacturing (AM) process called metal Big Area Additive Manufacturing (m-BAAM). To realize this goal, the PIs will detect and correct defects in the part as it is being printed by combining fundamental knowledge of the thermal physics and metallurgy of m-BAAM with in-process sensor data. Developed at the DOE-funded Manufacturing Demonstration Facility at Oak Ridge National Laboratory, the m-BAAM process involves one or more robots working together to produce a part by fusing metal wire layer-by-layer using arc welding. The process can print large metal parts such as turbine blades, which is not possible using other AM processes. In addition, m-BAAM production rates are more than ten times faster than other AM processes while requiring one-tenth of the material cost. Despite their potential to become a critical force multiplier in the energy generation industry, m-BAAM parts may fail to print accurately due to retention of heat and uneven cooling. Overheating and anomalous cooling rates in turn can cause inconsistencies in the microstructure, leading to sudden failure when used in safety-critical applications. In other words, flaw formation in m-BAAM parts is governed by the thermal history – intensity and spatial distribution of heat inside the part during printing. The thermal history is a complex function of the part shape and process settings such as welding energy, path taken by the welding torch for deposition (tool path), wire feed rate, among others.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Predicting Biomass Yields of Advanced Switchgrass Cultivars for Bioenergy and Ecosystem Services Using Machine Learning

The production of advanced perennial bioenergy crops within marginal areas of the agricultural landscape is gaining interest due to its potential to sustainably produce feedstocks for biofuels and bioproducts while also improving the sustainability and resilience of commodity crop production. However, predicting the biomass yields of this production system is challenging because marginal areas are often relatively small and spread around agricultural fields and are typically associated with various abiotic conditions that limit crop production. Machine learning (ML) offers a viable solution as a biomass yield prediction tool because it is suited to predicting relationships with complex functional associations. The objectives of this study were to (1) evaluate the accuracy of commonly applied ML algorithms in agricultural applications for predicting the biomass yields of advanced switchgrass cultivars for bioenergy and ecosystem services and (2) determine the most important biomass yield predictors. Datasets on biomass yield, weather, land marginality, soil properties, and agronomic management were generated from three field study sites in two U.S. Midwest states (Illinois and Iowa) over three growing seasons. The ML algorithms evaluated in the study included random forests (RFs), gradient boosting machines (GBMs), artificial neural networks (ANNs), K-neighbors regressor (KNR), AdaBoost regressor (ABR), and partial least squares regression (PLSR). Coefficient of determination (R 2 ) and mean absolute error (MAE) were used to evaluate the predictive accuracy of the tested algorithms. Results showed that the ensemble methods, RF (R 2 = 0.86, MAE = 0.62 Mg/ha), GBM (R 2 = 0.88, MAE = 0.57 Mg/ha), and GBM (R 2 = 0.78, MAE = 0.66 Mg/ha), were the most accurate in predicting biomass yields of the Independence, Liberty, and Shawnee switchgrass cultivars, respectively. This is in agreement with similar studies that apply ML to multi-feature problems where traditional statistical methods are less applicable and datasets used were considered to be relatively small for ANNs. Consistent with previous studies on switchgrass, the most important predictors of biomass yield included average annual temperature, average growing season temperature, sum of the growing season precipitation, field slope, and elevation. This study helps pave the way for applying ML as a management tool for alternative bioenergy landscapes where understanding agronomic and environmental performance of a multifunctional cropping system seasonally and interannually at the sub-field scale is critical.

09 BIOMASS FUELS↗