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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 OSTI2023

Will high-entropy carbides and borides be enabling materials for extreme environments?

Abstract The concept of multi-principal component has created promising opportunities for the development of novel high-entropy ceramics for extreme environments encountered in advanced turbine engines, nuclear reactors, and hypersonic vehicles, as it expands the compositional space of ceramic materials with tailored properties within a single-phase solid solution. The unique physical properties of some high-entropy carbides and borides, such as higher hardness, high-temperature strength, lower thermal conductivity, and improved irradiation resistance than the constitute ceramics, have been observed. These promising properties may be attributed to the compositional complexity, atomic-level disorder, lattice distortion, and other fundamental processes related to defect formation and phonon scattering. This manuscript serves as a critical review of the recent progress in high-entropy carbides and borides, focusing on synthesis and evaluations of their performance in extreme high-temperature, irradiation, and gaseous environments.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Micrometer: Micromechanics transformer for predicting full field mechanical responses of heterogeneous materials

Predicting mechanical responses of heterogeneous materials across scales remains a significant challenge. Traditional computational methods often struggle with complex and multiscale nature of these materials, limiting their effectiveness in real-world applications. Here, in this paper, we introduce Micrometer, a vision transformer based deep learning model designed to predict full field mechanical responses of heterogeneous materials, bridging the gap between computer vision and solid mechanics problems. We show that Micrometer, trained on a large-scale high-resolution dataset of 2D fiber-reinforced composites, can achieve state-of-the-art performance in predicting microscale strain fields across a wide range of material properties and loading conditions. Our model demonstrates accuracy and computational efficiency in applications such as computational homogenization and multiscale modeling, reducing computational time by up to two orders of magnitude compared to conventional numerical solvers while maintaining less than 1 % errors in predicting macroscale stress fields. Furthermore, we showcase Micrometer’s adaptability through transfer learning experiments on new materials with limited data, highlighting its potential to tackle diverse scenarios in computational solid mechanics. These results represent a significant step towards AI-driven innovation in materials science, addressing the limitations of traditional numerical methods and paving the way for more efficient simulations of heterogeneous materials across various industrial applications.

Composite materials↗
DOE OSTI2020

Uncovering the Relationship between Diameter and Height of Electrodeposited Lithium Protrusions in a Rigid Electrolyte

A promising approach for enabling rechargeable batteries with significantly higher energy densities than current lithium-ion batteries is by deploying lithium-metal anodes. However, the growth of lithium protrusions during charging presents significant challenges. Since these protrusions are often branched and filamentous in conventional liquid electrolytes, this problem is referred to in the literature as the “dendrite problem”. While solid electrolytes have the potential to solve this problem, protrusions grow in all electrolytes when the current density exceeds a critical value. Fundamentally understanding the formation is necessary to develop a rational approach for increasing the critical current density, but it is challenging due to the complex interplay between electrochemical and material properties. The diameters and heights of protrusions on lithium-metal anodes stabilized by a rigid block copolymer electrolyte were measured in situ by synchrotron hard X-ray microtomography. The diameter of the shorting protrusions increased linearly with increasing electrolyte thickness. Further, a universal linear relationship between protrusion height and diameter of both shorting and non-shorting protrusions was observed. A model based on the concentrated solution theory was used to establish the electrochemical and mechanical sources for our observations. Here, the computational analysis indicates that elastic and plastic deformation of both the lithium metal and the polymer are important to describe protrusion growth. Both stress-induced current density effects due to the deformation of the electrolyte near the protrusion and plastic deformation of lithium metal combine to give the counterintuitive result: the fastest-growing protrusions have the largest diameter.

25 ENERGY STORAGE↗
DOE OSTI2022

Measurement and Analysis of the Microphysical Properties of Arctic Precipitation Showing Frequent Occurrence of Riming

Detailed ground-based observations of snow are scarce in remote regions, such as the Arctic. Here, Multi-Angle Snowflake Camera measurements of over 55,000 solid hydrometeors—obtained during a two-year period from August 2016 to August 2018 at Oliktok Point, Alaska—are analyzed and compared to similar measurements from an earlier experiment at Alta, Utah. In general, distributions of hydrometeor fall speed, fall orientation, aspect ratio, flatness, and complexity (i.e., riming degree) were observed to be very similar between the two locations, except that Arctic hydrometeors tended to be smaller. In total, the slope parameter defining a negative exponential of the size distribution was approximately 50% steeper in the Arctic as at Alta. Sixty-six percent of particles were observed to be rimed or moderately rimed with some suggestion that riming is favored by weak boundary layer stability. On average, the fall speed of rimed particles was not notably different from aggregates. However, graupel density and fall speed increase as cloud temperatures approach the melting point.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Using Radioiodine Speciation to Address Environmental Remediation and Waste Stream Sequestration Problems at the Fukushima Daiichi Nuclear Power Plant and a DOE Site

Iodine-129 ( 129 I), with a half-life of half-life of 16 million years, is commonly considered the single greatest risk driver in high-level and low-level nuclear repositories. This risk stems from several basic properties of 129 I, and under many geochemical conditions, it can move as an anion at nearly the rate of water through the subsurface environment. 129 I is also extremely radiologically toxic because over 90% of body burden accumulates in the thyroid, which weighs only about 14g in an adult. There is also a large worldwide inventory of radioiodine as a result of its high fission yield and this inventory is rapidly increasing as a result of nuclear energy production. Radioiodine is produced at a rate of 40 GBq (1 Ci) per gigawatt of electricity produced by nuclear power. To illustrate how the properties of 129 I magnify its risk, 129 I accounts for only 0.00002% of the radiation released from the Savannah River Site in Aiken, South Carolina, but contributes 13% of the population dose, a six orders of magnitude magnification of risk with respect to its radioactivity. The currently favored solid phase for LLW immobilization is cement, while HLW immobilization is the incorporation of waste into glass (vitrification). However, so far, the incorporation into cement and subsequent leaching of only iodide has been seriously investigated. The major problem with this approach is that it ignores the complex speciation of iodine, i.e., it ignores iodate and organo-iodine which have different chemistries. Most of the past research was devoted to the mechanism of iodide uptake in cement hydrate phases that is sorption and/or incorporation. Very few data exist on iodate and organo-iodine incorporation in cement, even though large quantities of liquid waste containing also radioiodine have already been solidified in cement Iodine-129 from low-level waste is commonly disposed of in cementitious materials. Grout, a dense cementitious fluid, mixed with a reducing slag, is often used to immobilize radionuclides. However, the reducing environment might not be conducive to immobilize iodine. For example, the silver based immobilization technologies (e.g., AgCl, Ag-impregnated granular activated carbon, Ag-mordenite) remove iodine from the aqueous phase by promoting the formation of Ag-iodide precipitates. The solubility of AgI is eight orders of magnitude lower than it is for AgIO 3 . Similarly, coprecipitation of iodine into calcium carbonate phases occurs only with IO 3 - and not with I - and org-I. If one would want to immobilize iodine more effectively, different engineering approaches would need to be used to promote binding of I - , IO 3 - , or organo-I. Using laboratory experiments with grout, slag, and silver-based adsorbents, and GC-MS and I K-edge XANES and EXAFS and C K-edge XANES spectroscopy for identifying iodine speciation, the major problems with these methods have been identified as focused too much on just one of the iodine species for immobilization, while others, especially organo-I, remained mobile. Finally, we established that most of the adsorbents that are used contain sufficient amounts of organic matter to create organo-I . It is anticipated that increased attention directed at understanding and quantifying the speciation of radioiodine, as opposed to simply total radioiodine, will lead to improved remediation results to be used for long-term radioiodine disposal in cementitious waste forms.

129-Iodine↗
DOE OSTI2020

Progress in Understanding the Origins of Excellent Corrosion Resistance in Metallic Alloys: From Binary Polycrystalline Alloys to Metallic Glasses and High Entropy Alloys

Some of the factors responsible for good corrosion resistance of select polycrystalline and emerging alloys in chloride solutions are discussed with a goal of providing some perspectives on the current status and future directions. Traditional metallic glass alloys, single phase high entropy alloys (HEAs), early metallic glasses, and high entropy metallic glasses are all emerging corrosion-resistant alloys (CRAs) that utilize traditional strategies for improved corrosion resistance as well as take advantage of some other novel beneficial attributes. These materials enjoy many degrees of freedom as far as choice of both composition and structure, providing great flexibility in the pursuit of superior corrosion resistance. The new materials depart from classical solvent-solute type polycrystalline binary or ternary alloys. Thus, such emerging materials provide significant opportunities to achieve even greater improvements in corrosion resistance in harsh environments. Several examples of the unique corrosion properties of selected materials in the context of modern theories of corrosion are discussed herein. Discussion is restricted to solid-solution binary or ternary polycrystalline alloys, several metallic glass alloys, and single phase HEAs. A common feature of many CRAs is that composition and microstructure often affect both passivity and resistance to localized corrosion that can be divided into initiation, stabilization, and propagation stages. Enormous complexities in protective oxide structures and chemistries and the large number of combinatorial possibilities in newer materials such as HEAs preclude trial-and-error approaches and perhaps even combinatorial experimental design. Computational materials methodologies will be required in the search for new corrosion-resistant alloys in these material classes. The search must consider the best scientific insights available regarding how major and minor alloy additions, as well as various microstructural attributes, contribute to corrosion mitigation. Additional scientific insights, as they emerge, will enable choices beyond the reliance on high concentrations of alloying elements that are known to affect passivity breakdown and pit stabilization. A challenge is to connect the “basic attributes” of an alloy with its properties. The strength of this connection will likely require new scientific principles enabling deep multiphysics insights in order to link feature(s) such as composition and metallurgical phases to the desired corrosion properties. Application of data informatics will likely also play a role given the plethora of variables that are important in corrosion and the difficulty in assessing all relationships. Here, the opportunity exists to accelerate the design of emerging materials for high corrosion resistance.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Predictive Tools for Customizing Heat Treatment of Additively Manufactured Aerospace Components

Laser-bed powder fusion (LBPF) additive manufacturing is increasingly being used to produce components of complex geometries using the Ni-base superalloy Inconel 718. The composition and the microstructure of the alloy are currently well optimized for wrought components made using conventional manufacturing processes such as rolling, forging, extrusion, etc. The attractive mechanical properties of the alloy result from the underlying austenitic matrix with fine equiaxed grains, and a high density and uniform distribution of the precipitation hardening phase, γ". Heat treatment steps such as homogenization, solutioning and aging are well documented for the wrought alloy. However, when the same wrought alloy compositions are used for the additive manufacturing (AM) processes, the asprocessed microstructure is significantly different, because of the different thermal history associated with LBPF, including rapid solidification and multiple temperature excursions that lead to multiple re-melting and reheating in the solid state. Rapid solidification introduces potential non-equilibrium effects at the moving solid-liquid interfaces that impact the extent of solute segregation, as well as the morphology of the dendritic grains that form. In order to recover the target mechanical properties, AM components have to undergo post-process heat treatments. However, such heat treatments have to be custom designed for the AM process and the component geometry because of the expected vast differences in the microstructure at various locations of a component with complex geometry. The homogenization and precipitation steps should be optimized for the component so that target mechanical properties can be obtained throughout the part. The objective of this research is to utilize High Performance Computing in phase field simulations of microstructure evolution during post-processing of AM components. The physics-based modeling will be beneficial in reducing the experimental effort required for heat treatment process selection, optimization, and certification, thus leading to a significant reduction in energy consumption for AM and post-processing heat treatment. The optimization study will help identify heat treatments steps that are critical for development of a final desired microstructure with the minimum energy input. This combined with shortening of the production cycle (time-to-market) by reducing the number of failed parts (property targets), and reduction in the number of iterations for process optimization, will enable 30-40% savings in the energy costs. Phase field simulations of the degree of homogenization and the effect of local matrix composition on the nucleation and growth of competing precipitating phases were performed using the Microstructure Evolution Using Massively Parallel Phase Field Simulations code developed in-house at the Oak Ridge National Laboratory. The simulations were able to successfully capture the kinetics of nucleation and growth, and morphologies of various precipitating phases as a function of local matrix compositions and composition gradients characteristic of local microstructures arising from location-dependent variations in the thermal conditions. Future work will involve extending the simulations to a length scale consisting of multiple dendrites, so that the effect of homogenization on the coarsening of the dendrites can be simulated and used as an additional input to the optimization of the heat treatment process.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Group hexavalent actinide separation from lanthanides using sodium bismuthate chromatography

Advanced used nuclear fuel (UNF) reprocessing strategies are limited by the complex radiochemical separations and engineering required to achieve the separation of actinides (An) from neutron scavenging lanthanides (Ln). The accessibility of the hexavalent oxidation state for the actinides (U – Am) provides a pathway to achieving a group hexavalent actinide separation from the trivalent lanthanides and Cm. The solid oxidant and ion exchanger, sodium bismuthate (NaBiO 3 ), has been demonstrated to quantitatively oxidize and separate Am from trivalent Cm in a column chromatographic system. This work expands on the use of NaBiO 3 chromatography to characterize the adsorption, kinetic, and elution behavior of U, Pu, and Eu. Separation factors over 200 with rapid kinetics were observed at dilute nitric acid concentrations with a complete An/Ln separation achieved in under an hour. In conclusion, the adsorption and chromatographic behavior of key fission products present in various reprocessing raffinates was characterized which demonstrated potential application of a NaBiO 3 -based separation following a TRUEX process.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2025

A Synchrotron-Based Vacuum Ultraviolet Photoionization Mass Spectrometer-Coupled Microreactor To Probe Thermocatalysis

Vacuum ultraviolet photoionization (VUV-PI) mass spectrometry offers an isomer-selective and universal ionization with minimal fragmentation detection of organics in complex chemical systems such as pyrolysis and combustion. Here, in this study, we report a state-of-the-art experimental setup of a universal catalytic microreactor combined with a molecular beam to investigate the thermocatalytic oxidation of a heterogeneous system relevant for probing reactions at gas–solid interfaces. In strong contrast to traditional off-line analytical methods, this technique is capable of identifying and quantifying short-lived species (radicals) as well as stable products to decipher initial reaction steps via the detection of nascent products. The thermocatalytic oxidative degradation of exo-tetrahydrodicyclopentadiene (JP-10), a high energy-density hydrocarbon fuel, over solid titanium–aluminum–boron reactive mixed metal nanopowder (Ti-Al-B RMNP) is exploited to showcase potential applications. Overall, some 59 nascent gas-phase products are identified via photoionization efficiency (PIE) curves, including oxygenated species and hydrocarbons comprising closed-shell molecules and radicals. The critical temperature for complete oxidative decomposition of JP-10 was lowered by 450 K from 1400 K to 950 K, indicating an efficient thermocatalytic action of Ti-Al-B nanoparticles on JP-10. The enabling of a universal chemical microreactor along with VUV-PI mass spectrometry broadens the applicability of this technique to hydrocarbon fuel oxidation and pyrolysis characterization. This isomer-selective sensitive probing along with the detection of radical transients makes the aforementioned technique superior to other conventional analytical techniques such as microflow tube and pyrolysis-gas chromatography coupled with mass spectrometry for investigating similar pyrolysis reactions and comprehensive quantification.

Paul, Dababrata [Univ. of Hawaii at Manoa, Honolul↗
DOE OSTI2025

Radiation Effects on Uranyl Tetrachloro Coordination Compounds: Impact of Lattice Water

Nuclear materials, such as uranium-bearing solids, are exposed to high levels of ionizing radiation throughout the nuclear fuel cycle; thus, it is important to develop a molecular-level understanding of how these materials behave and degrade in the presence of gamma (γ) irradiation. In the current study, three U(VI) tetrachloride complexes, M 2 [UO 2 Cl 4 ]·xH 2 O (where M = K + , Rb + , or Cs + and x = 0 or 2), and their respective chloride salts were exposed to 1–50 kGy of γ radiation using a 60 Co source. Irradiated materials were evaluated by using electron paramagnetic resonance (EPR) and Raman spectroscopy and were further explored by using density functional theory (DFT) methods. EPR spectra of the irradiated materials suggest the formation of a Cl-based radical for both the alkali salts and the uranyl tetrachloride compounds, and DFT calculations provide evidence that the Cl 2 –• radical is formed within these materials. The presence of water in the K + and Rb + compounds leads to additional spectroscopic signatures that could be traced back to water radiolysis and the formation of peroxide and superoxide species. DFT results support the formation of HO 2 • in the lattice and potentially the formation of a [UO 2 Cl 3 (O 2 )] 3– species, highlighting the impact of water within the hydrated material to alter U(VI) speciation by radiolysis.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Probing soft X-ray induced photoreduction of a model Mn-complex at cryogenic conditions

Soft X-ray absorption spectroscopy of first row transition elements at their respective L -edges provides important information about the oxidation and spin states of the metal centers. However, the associated sample damage in radiation-sensitive samples substantially alters the electronic and chemical structures of redox-active metal centers. Here, we measure the soft X-ray spectrum of the model Mn III (acac) 3 complex containing a redox-active Mn III metal center in an octahedral environment with a superconducting transition-edge sensor detector. To reduce the secondary damage resulting primarily from the diffusion of radicals and electrons, the spectra are collected at 30 K and 80 K on solid samples. Starting from the first scan, we detect the contribution of X-ray induced sample damage leading to a change in the Mn II intensity. However, at low temperatures, particularly at 30 K, we do not observe a gradual increase in the radiation damage with successive scans with the X-ray beam at the same spot. At our estimated dose of 90 kGy, we find 62% of Mn III (acac) 3 is still intact at 30 K. However, at room temperature, we see a gradual increase in radiation damage with increasing numbers of scans at the same spot, which is consistent with the possibility of increased diffusion rates of secondary radicals and electrons as noted in other studies.

L-edge spectroscopy↗
DOE OSTI2021

Physics-Informed Neural Networks for Heat Transfer Problems

Abstract Physics-informed neural networks (PINNs) have gained popularity across different engineering fields due to their effectiveness in solving realistic problems with noisy data and often partially missing physics. In PINNs, automatic differentiation is leveraged to evaluate differential operators without discretization errors, and a multitask learning problem is defined in order to simultaneously fit observed data while respecting the underlying governing laws of physics. Here, we present applications of PINNs to various prototype heat transfer problems, targeting in particular realistic conditions not readily tackled with traditional computational methods. To this end, we first consider forced and mixed convection with unknown thermal boundary conditions on the heated surfaces and aim to obtain the temperature and velocity fields everywhere in the domain, including the boundaries, given some sparse temperature measurements. We also consider the prototype Stefan problem for two-phase flow, aiming to infer the moving interface, the velocity and temperature fields everywhere as well as the different conductivities of a solid and a liquid phase, given a few temperature measurements inside the domain. Finally, we present some realistic industrial applications related to power electronics to highlight the practicality of PINNs as well as the effective use of neural networks in solving general heat transfer problems of industrial complexity. Taken together, the results presented herein demonstrate that PINNs not only can solve ill-posed problems, which are beyond the reach of traditional computational methods, but they can also bridge the gap between computational and experimental heat transfer.

Engineering↗
DOE OSTI2026

Robust Containment Queries over Collections of Trimmed NURBS Surfaces via Generalized Winding Numbers

Here, we propose a containment query that is robust to the watertightness of regions bound by trimmed NURBS surfaces, as this property is difficult to guarantee for in-the-wild CAD models. Containment is determined through the generalized winding number (GWN), a mathematical construction that is indifferent to the arrangement of surfaces in the shape. Applying contemporary techniques for the 3D GWN to trimmed NURBS surfaces requires some form of geometric discretization, introducing computational inefficiency to the algorithm and even risking containment misclassifications near the surface. In contrast, our proposed method leverages properties of the 3D solid angle to solve the relevant surface integral using a boundary formulation with rapidly converging adaptive quadrature. Batches of queries are further accelerated by memoizing (i.e., caching and reusing) quadrature node positions and tangents as they are evaluated. We demonstrate that our GWN method is robust to complex trimming geometry in a CAD model, and is accurate up to arbitrary precision at arbitrary distances from the surface. The derived containment query is therefore robust to model non-watertightness while respecting all curved features of the input shape.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Topical Report on Water Balance Model Development and Initial Application

This topical report summarizes results from developing a generic dynamic water balance tool which can be used by coal-fired power plant operators to evaluate FGD water management strategies. EPRI is using a statistical software model created on the GoldSim platform and data from coal-fired energy plants to determine the fraction of power plant water needs that can be satisfied by electrodialysis reversal treatment and, correspondingly, the discharge volume and/or byproducts that still need to be managed. The generic model is designed to accept a number of easily obtainable process variables and generate probabilities of resultant water characteristics. The model is also constructed as a universal process flow; one that can be adjusted to accommodate most major components of an FGD blowdown handling system. The generic model is dynamic and utilizes user-inputted operational variation in water quality and quantity to best provide a probabilistic representation of potential performance outcomes. In order to account for complexities of water composition and treatment, a separate chemical modeling software is used to evaluate water chemistry and unit operation performance. The thermodynamic modeling software, OLI Studio, is employed to convert ion data from laboratory analyses into balanced molecular concentrations that are classified as dissolved and suspended solids. The dynamic water model is designed to import a total of 11 species commonly found in FGD blowdown water as molecular concentrations.Water management unit operations are used in the model and categorized by either physical unit operation blocks or chemical unit operations blocks. As the name implies, the physical water treatment blocks are unit operations that are exclusively physical changes to the system (e.g., separations and volume changes). The chemical water treatment blocks are unit operations where chemical changes occur and require the use of OLI Studio to characterize subsequent streams. Water and material balances from three unique coal-fired power plants were obtained and preliminary modeling on the dynamic water balance tool was performed using the extracted data from these power plants. These three power plants were chosen due to their unique configurations and water profiles that represented a variety of operational changes. Results from using the dynamic tool with the three plant configurations are presented and discussed in the context of the existing tool and planned future capabilities.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2021

Structural Polymorphism of Chitin and Chitosan in Fungal Cell Walls From Solid-State NMR and Principal Component Analysis

Chitin is a major carbohydrate component of the fungal cell wall and a promising target for novel antifungal agents. However, it is technically challenging to characterize the structure of this polymer in native cell walls. Here, we recorded and compared 13 C chemical shifts of chitin using isotopically enriched cells of six Aspergillus, Rhizopus, and Candida strains, with data interpretation assisted by principal component analysis (PCA) and linear discriminant analysis (LDA) methods. The structure of chitin is found to be intrinsically heterogeneous, with peak multiplicity detected in each sample and distinct fingerprints observed across fungal species. Fungal chitin exhibits partial similarity to the model structures of α- and γ-allomorphs; therefore, chitin structure is not significantly affected by interactions with other cell wall components. Addition of antifungal drugs and salts did not significantly perturb the chemical shifts, revealing the structural resistance of chitin to external stress. In addition, the structure of the deacetylated form, chitosan, was found to resemble a relaxed two-fold helix conformation. This study provides high-resolution information on the structure of chitin and chitosan in their cellular contexts. The method is applicable to the analysis of other complex carbohydrates and polymer composites.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Flexible machine-learning interatomic potential for simulating structural disordering behavior of Li 7 La 3 Zr 2 O 12 solid electrolytes

Batteries based on solid-state electrolytes, including Li 7 La 3 Zr 2 O 12 (LLZO), promise improved safety and increased energy density; however, atomic disorder at grain boundaries and phase boundaries can severely deteriorate their performance. Machine-learning (ML) interatomic potentials offer a uniquely compelling solution for simulating chemical processes, rare events, and phase transitions associated with these complex interfaces by mixing high scalability with quantum-level accuracy, provided that they can be trained to properly address atomic disorder. To this end, we report the construction and validation of an ML potential that is specifically designed to simulate crystalline, disordered, and amorphous LLZO systems across a wide range of conditions. The ML model is based on a neural network algorithm and is trained using ab initio data. Performance tests prove that the developed ML potential can predict accurate structural and vibrational characteristics, elastic properties, and Li diffusivity of LLZO comparable to ab initio simulations. As a demonstration of its applicability to larger systems, we show that the potential can correctly capture grain boundary effects on diffusivity, as well as the thermal transition behavior of LLZO. Here these examples show that the ML potential enables simulations of transitions between well-defined and disordered structures with quantum-level accuracy at speeds thousands of times faster than ab initio methods.

25 ENERGY STORAGE↗
DOE OSTI2025

Many-body interference in kagome crystals

When electrons in metals act collectively, they enable emergent phenomena and electronic functionalities that transcend the behaviour of individual particles. Coherent collective charge motion has so far been observed primarily in superconductors, in which it arises with the formation of Cooper pairs. Here we report experimental evidence for coherent charge transport in the normal state of the kagome metal CsV 3 Sb 5 , indicative of a distinct collective electronic state. The signature is a set of magnetoresistance oscillations in mesoscopic crystalline pillars under in-plane magnetic fields, with a periodicity determined by the number of magnetic flux quanta h/e threading between adjacent kagome layers—effectively forming an interlayer Aharonov–Bohm interferometer. The cooperative nature of this phenomenon is evidenced by a non-analytic angular dependence characterized by abrupt transitions between discrete oscillation frequencies and its persistence over length scales that exceed the single-particle mean free path. Notably, the oscillation amplitude matches other anomalous electronic responses reported in CsV 3 Sb 5 , pointing to an underlying mechanism that establishes intrinsic coherence. These findings shed new light on the debated nature of correlated order in kagome metals and establish CsV 3 Sb 5 as a platform for realizing long-range coherent charge transport in the absence of superconductivity—opening new directions for coherence in correlated electron systems beyond conventional models.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Computational Design of Alloys for Energy Technologies

Advanced materials that maintain their mechanical performance under elevated temperatures, corrosive environments, and a range of static and evolving stresses are needed to improve the efficiency and reduce the environmental impact of a wide spectrum of energy technologies. For instance, cost-efficient alloys that can withstand high temperatures (e.g., 700 °C) have a critical role in improving the efficiency and economics of power generation to support decarbonization of the energy sector; such is true of both the nuclear and fossil energy sectors. Considering both the threats of the energy crisis, namely soaring costs of greenhouse gas emission-producing energy and climate change, it is essential to increase the pace of material discovery and enable rapid paths for material qualification to advance clean energy technologies. Conventionally, alloy development has followed a slow Edisonian process that uses repeated cycles of making, characterizing, and modifying to arrive at optimum composition and processing conditions to achieve the desired component performance. This optimization is followed by the necessary stepwise materials qualification. Furthermore, the increasing adoption of sound data management and physics-informed machine learning represents the next step in the acceleration of materials design and development. In the integrated computational materials engineering (ICME) approach, computational modeling and simulation data from different length and time scales can be combined with complex microstructural details from multimodal experimental characterization and selective property testing to close the design loop for rapid alloy development.

Computational Design Of Materials↗