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At least 127 records · Page 7

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DOE OSTI2024

Driving macro-scale transformations in three-dimensional-printed biopolymers through controlled induction of molecular anisotropy at the nanoscale

Motivated by the need to harness the properties of renewable and biodegradable polymers for the design and manufacturing of multi-scale structures with complex geometries, we have employed our additive manufacturing platform that leverages molecular self-assembly for the production of metre-scale structures characterized by complex geometries and heterogeneous material composition. As a precursor material, we used chitosan, a chemically modified form of chitin, an abundant and sustainable structural polysaccharide. We demonstrate the ability to control concentration-dependent crystallization as well as the induction of the preferred orientation of the polymer chains through the combination of extrusion-based robotic fabrication and directional toolpathing. Anisotropy is demonstrated and assessed through high-resolution micro-X-ray diffraction in conjunction with finite element simulations. Using this approach, we can leverage controlled and user-defined small-scale propagation of residual stresses to induce large-scale folding of the resulting structures.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Group-Additivity–Embedded Multiscale Modeling for Electric Field-Enhanced Nanocatalysis

Elucidating structure-performance relationships remains a central challenge in field-enhanced catalysis, where nanoparticles exhibit nonuniform surface sites with site-dependent responses to electric fields. Low-coordination sites (edges, corners, and tips) are particularly electric field-sensitive (EF), leading to nonuniform charge distribution, adsorption energies, and catalytic activity. Here, using ammonia decomposition on a ruthenium cluster as a model system, we develop a transferable multiscale framework integrating density functional theory, group additivity (GA), Brønsted-Evans-Polanyi scaling, and microkinetic modeling to predict EF-dependent activity across nonuniform cluster sites. Across sites and fields, the nitrogen adsorption energy (E N ) emerges as the governing descriptor, yielding robust volcano relationships whose optimum shifts systematically with field: negative fields strengthen N binding via electron accumulation, while positive fields weaken N binding via charge depletion, moving the optimal E N toward weaker binding. Microkinetic analysis shows that N≡N bond formation remains the key kinetic bottleneck over most conditions; positive fields lower the effective barrier and, critically, increase the fraction of near-optimal active sites, leading to a net enhancement in overall activity relative to zero-field and negative-field cases. By capturing EF- and site-dependent energetics with high accuracy and low computational cost, this GA-embedded multi-scale simulation workflow provides a physically interpretable route to predict and design field-enhanced nanocatalysis.

ammonia decomposition↗

DOE OSTI2025

Unraveling the origin of air-stability in single-crystalline layered oxide positive electrode materials

Single-crystalline Ni-rich layered oxides present compelling advantages over conventional polycrystalline counterparts toward large-scale applications, including enhanced mechanical stability and higher energy density. Nevertheless, the deleterious effects of air exposure, which is inevitable in industrial processing, on their structure and electrochemical performance remain poorly understood. Herein, we reveal that air exposure is more detrimental to the electrochemical performance of single-crystalline layered oxide positive electrodes than polycrystalline counterparts. It is found that air-induced surface structural distortions are primarily responsible for the electrochemical performance decay of single-crystalline samples rather than the generally believed surface residual lithium. Leveraging multiscale diffraction and imaging techniques, we identify an undesirable structural transition to a metastable O1* phase, which introduces substantial lattice defects and localized strain concentrations within the layered structure. These adverse structural evolutions compromise structural integrity and promote crack initiation during electrochemical cycling, ultimately accelerating capacity fade. Our findings provide critical insights into the air-induced degradation mechanisms and emphasize the urgent need for developing effective stabilization strategies to facilitate the commercial implementation of single-crystalline Ni-rich positive electrodes.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Parasitic structure defect blights sustainability of cobalt-free single crystalline cathodes

Abstract Recent efforts to reduce battery costs and enhance sustainability have focused on eliminating Cobalt (Co) from cathode materials. While Co-free designs have shown notable success in polycrystalline cathodes, their impact on single crystalline (SC) cathodes remains less understood due to the significantly extended lithium diffusion pathways and the higher-temperature synthesis involved. Here, we reveal that removing Co from SC cathodes is structurally and electrochemically unfavorable, exhibiting unusual voltage fade behavior. Using multiscale diffraction and imaging techniques, we identify lithium-rich nanodomains (LRNDs) as a heterogeneous phase within the layered structure of Co-free SC cathodes. These LRNDs act as critical tipping points, inducing significant chemo-mechanical lattice strain and irreversible structural degradation, which exacerbates the voltage and capacity loss in electrochemical performance. Our findings highlight the considerable challenges of developing Co-free SC cathodes compared to polycrystalline ones and emphasize the need for new strategies to balance the interplay between cost, sustainability, and performance.

36 MATERIALS SCIENCE↗

DOE OSTI2023

The confluence of machine learning and multiscale simulations

Multiscale modeling has a long history of use in structural biology, as computational biologists strive to overcome the time- and length-scale limits of atomistic molecular dynamics. Contemporary machine learning techniques, such as deep learning, have promoted advances in virtually every field of science and engineering and are revitalizing the traditional notions of multiscale modeling. Deep learning has found success in various approaches for distilling information from fine-scale models, such as building surrogate models and guiding the development of coarse-grained potentials. However, perhaps its most powerful use in multiscale modeling is in defining latent spaces that enable efficient exploration of conformational space. In conclusion, this confluence of machine learning and multiscale simulation with modern high-performance computing promises a new era of discovery and innovation in structural biology.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

A Perspective on Multiscale Modeling of Explicit Solvation-Enabled Simulations of Catalysis at Liquid–Solid Interfaces

Catalysis at liquid-solid interfaces is profoundly influenced by the interfacial solvent structure, which affects catalytic activity, selectivity, and reaction pathways. This perspective discusses state-of-the-art multiscale modeling methods that integrate quantum mechanics and molecular mechanics approaches to apply explicit solvent molecules to capture these interfacial phenomena. Specifically, the construction of multiscale models, the importance of capturing the interfacial solvent structure, and the computational strategies used to achieve this are explored, and the challenges in balancing chemical accuracy with computational expense are highlighted. Additionally, this perspective addresses the limitations of current methods. Opportunities for integrating machine learning are proposed. Here, by advancing the efficiency and user friendliness of multiscale modeling, it is argued that deeper insights into heterogeneous catalysis in liquid phases can be provided, which will ultimately contribute to the development of more efficient catalytic processes.

Ab initio molecular dynamics↗

DOE OSTI2025

Macro-micro multiscale modeling to assist the design of HPDC Al castings microstructure and alloys for EV super-large body structures (Phase 1)

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2025

Macro-micro multiscale modeling to assist the design of HPDC Al castings microstructure and alloys for EV super-large body structures (Phase 1)

Implementation of High Pressure Die Casting (HPDC) Aluminum (Al) body structures for high volume electrified vehicles (EV) to improve electric efficiency remains a key strategy within many original equipment manufacturer (OEM)s. In addition to high strength for safety requirements, superior Self-Piercing Riveting (SPR) performance is demanded for HPDC Al alloys to be compatible with high volume SPR joining. In this work, it is proposed to extend and validate an existing Contractor finite element multiscale macro-micro modeling approach to quantify the influence of the microstructure of HPDC alloys on the fracture strain/displacement under 3-point bend and clinch testing. The success of this work will allow to replace solution treatment stage with low energy consumption heat treatment (HT) processes, or to design new non heat treatable (NHT) HPDC Al alloys to eliminate HT requirements. Ultimately, this project will facilitate the application of HPDC Al alloys for super-large vehicle structures to significantly reduce vehicle weight, and thus improving energy efficiency. The purpose of this project is to extend and validate an existing finite element code, which is based on the Contractor developed macro-micro multi-scale modeling approach, to numerically simulate the three-point bending and clinch test and study the influences of material microstructural characteristics and phase properties on the rivetability. The macro-micro modeling approach begins with a sample scale model and identify the location, which is mostly prone to failure, the deformation history of the boundaries of that location calculated will be used to drive a microstructure-based sub-models where the material microstructure and microscale properties are considered. Using this approach, the wrap-bending failure for two Al alloys are correctly predicted for the first time. This will start with phase I effort of building a framework of macro-micro three point bending test and clinch test of Al10SiMgMn HPDC alloy in the as-cast and T7 heat treated conditions. Those results will then be validated with experimental test results. The phase II effort will involve the utilization of the knowledge learned in phase I to establish the quantitative correlation between the microstructure characteristics and the riveting performance, which will be further used to guide the optimization of HPDC Al alloy microstructure using heat treatment process to achieve sufficient rivetability to join large thin-wall HPDC alloys.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Strong, ductile, and hierarchical hetero-lamellar-structured alloys through microstructural inheritance and refinement

The strength−ductility trade-off exists ubiquitously, especially in brittle intermetallic-containing multiple principal element alloys (MPEAs), where the intermetallic phases often induce premature failure leading to severe ductility reduction. Hierarchical heterogeneities represent a promising microstructural solution to achieve simultaneous strength−ductility enhancement. However, it remains fundamentally challenging to tailor hierarchical heterostructures using conventional methods, which often rely on costly and time-consuming processing. Here, we report a multiscale microstructural inheritance and refinement strategy to process “structural hierarchy precursors” in as-cast heterogeneous Al 0.7 CoCrFeNi MPEAs, which lead directly to a hierarchical hetero-lamellar structure (HLS) after simple rolling and annealing. Interestingly, it takes only 10 min of annealing time, two orders of magnitude less than that required to render the state-of-the-art properties during conventional processing of Al 0.7 CoCrFeNi, for us to achieve record-high strength−ductility combinations via the hierarchical HLS design that sequentially stimulates multiple unusual deformation and reinforcement mechanisms. In particular, the HLS-enabled high hetero-deformation-induced (HDI) internal stress triggers profuse <111>-type dislocations on over five independent slip systems in the supposedly brittle intermetallic phase and activates extensive stacking faults (SFs) and nanotwinning in the adjoining soft phase with a rather high SF energy. These unexpected, dynamically reinforcing hetero-deformation mechanisms across multiple length scales facilitate high sustained HDI strain hardening, along with a salient microcrack-mediated extrinsic ductilization effect, suggesting that the proposed microstructural inheritance and refinement strategy provides an efficient, fast, and low-cost approach to overcome the strength−ductility trade-off in a broad range of structural materials.

Science & Technology - Other Topics↗

DOE OSTI2020

Perspectives on Quenching and Tempering 4340 Steel

Steels are ubiquitous due to their affordability and the landscape of useful properties that can be generated for engineering applications. But to further expand the performance envelope, one must be able to understand and control microstructure development by alloying and processing. In this paper we use multiscale, advanced characterization to better understand the structural and chemical evolution of AISI 4340 steel after quenching and tempering (Q&T), including the role of quench rate and short-time, isothermal tempering below 573 K (300 °C), with an emphasis on carbide formation. We compare the microstructure and/or property changes produced by conventional tempering to those produced by higher temperature, short-time “rapid” tempering. We underscore that no single characterization technique can fully capture the subtle microstructure changes like carbon redistribution, transition carbide and/or cementite formation, and retained austenite decomposition that occur during Q&T. Only the use of multiple techniques begins to unravel these complexities. After controlled fast or slow quenching, η transition carbides clearly exist in the microstructure, likely associated with autotempering of this high martensite start temperature (Ms) steel. Isothermal tempering below 598 K (325 °C) results in the relief of carbon supersaturation in the martensite, primarily by the formation of η transition carbides that exhibit a range of carbon levels, seemingly without substitutional element partitioning between the carbide and matrix phases. Hägg transition carbide is present between 300 °C and 325 °C. After conventional tempering at or above 598 K (325 °C) for 2 h, cementite is predominant, but small amounts of cementite are also present in other conditions, even after quenching. Previous work has indicated that silicon (Si) and substitutional elements partition between the cementite, which initially forms under paraequilibrium conditions, and the matrix. Phosphorous (P) may also be preferentially located at cementite/matrix interfaces after high temperature tempering. Slower quench rates result in greater amounts of retained austenite compared to those after fast quenching, which we attribute to increased austenite stability resulting from “autopartitioning”. Rapid, high temperature tempering is also found to diminish tempered martensite embrittlement (TME) believed to be associated with the extent of austenite decomposition, resulting in mechanical properties not attainable by conventional tempering, which may have important implications with respect to industrial heat treatment processes like induction tempering. Controlling the amount and stability of retained austenite is not only relevant to the properties of Q&T steels, but also next-generation advanced high strength steels (AHSS) with austenite/martensite mixtures.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Denoising diffusion algorithm for inverse design of microstructures with fine-tuned nonlinear material properties

Here we introduce a denoising diffusion algorithm to discover microstructures with nonlinear fine-tuned properties. Denoising diffusion probabilistic models are generative models that use diffusion-based dynamics to gradually denoise images and generate realistic synthetic samples. By learning the reverse of a Markov diffusion process, we design an artificial intelligence to efficiently manipulate the topology of microstructures to generate a massive number of prototypes that exhibit constitutive responses sufficiently close to designated nonlinear constitutive behaviors. To identify the subset of microcstructures with sufficiently precise fine-tuned properties, a convolutional neural network surrogate is trained to replace high-fidelity finite element simulations to filter out prototypes outside the admissible range. Results of this study indicate that the denoising diffusion process is capable of creating microstructures of fine-tuned nonlinear material properties within the latent space of the training data. More importantly, this denoising diffusion algorithm can be easily extended to incorporate additional topological and geometric modifications by introducing high-dimensional structures embedded in the latent space. Numerical experiments are conducted on the open-source mechanical MNIST data set (Lejeune, 2020). Consequently, this algorithm is not only capable of performing inverse design of nonlinear effective media, but also learns the nonlinear structure–property map to quantitatively understand the multiscale interplay among the geometry, topology, and their effective macroscopic properties.

42 ENGINEERING↗

DOE OSTI2024

Light scattering by V 4 O 7 film across the metal–insulator transition

The experimental study of the angle-resolved hemispherical light scattering by V 4 O 7 film within a broad temperature range across metal–insulator transition reveals complex structural reorganization of the film deposited on the c-cut sapphire crystal. The bidirectional scattering distribution function and the surface autocorrelation function were obtained from scattering data to visualize statistics of the spatially resolved contributions of optical inhomogeneities in normal and lateral directions to the surface. The measurements reveal an anisotropic surface roughness distribution due to the twinned domain structure, with significant anisotropy changes across the phase transition. The V 4 O 7 film deposited on sapphire leads to a polydomain structure, minimizing elastic strain energy with distinct multiscale distributions of surface domains. Near T c , the material shows the lowest roughness but the highest lateral disorder of the surface.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Development of Composite Photocatalyst Materials that are Highly Selective for Solar Hydrogen Production and their Evaluation in Z-Scheme Reactor Designs

The key technology gap preventing a vertically stacked dual-bed particle suspension reactor from achieving the DOE MYRD&D ultimate cost target for H 2 production remains the lack of materials in particle form factor that exhibit ≥10% solar-to-H 2 energy conversion (STH) efficiency as a suspension. Therefore, our project goals centered around strategies to increase the STH efficiency by enhancing photophysical properties of perovskite oxide particles including increased visible-light absorption, increased selectivity for electrocatalysis of the H 2 evolution reaction (HER) and the O 2 evolution reaction (OER) through development of ultrathin oxide coatings, correlating composition and structure to function, and improving understanding of multiscale transport and kinetic processes.

08 HYDROGEN↗

DOE OSTI2024

Addressing Critical Problems in Materials Science Through Multiscale and Multimode Characterization (Project 1); Characterization and Optimization of Novel Triple-Conducting Oxide Materials for Energy Applications (Project 2) (CRADA Final Report)

PROJECT 1: Address critical problems in materials science and simultaneously advance the state-of-the-art in multiscale and multimode characterization using the combined advanced analytical capabilities and expertise of Colorado School of Mines (CSM) and the National Renewable Energy Laboratory (NREL). The primary effort of the Phase I of this CRADA is to establish the International Center for Multiscale Characterization using shared resources at both NREL and CSM. Phase II will focus on capability development and marketing, choosing candidate materials science issues in the areas of structure imaging, chemical composition mapping, and correlating properties and performance of materials for impact in energy-related, environmental and critical materials areas. The CRADA will be modified to include specific topics of concern in materials science to industry member partners. Advanced analytical capabilities and expertise at CSM and NREL will be used to advance materials understanding and performance through characterization of multiscale phenomena including structural imaging, chemical composition mapping, and other techniques correlating properties and performance of materials. PROJECT 2: As part of the International Center for Materials Characterization, work under Modification #1 will be led by Colorado School of Mines (CSM), working in collaboration with NREL staff to mentor and advise CSM postdoctoral researchers on set up of diffusion annealing experiments. The purpose of the modification is to provide for NREL staff to mentor and advise CSM postdoctoral researchers on set up of diffusion annealing experiments, including mentoring and advising the CSM-NREL team on proper Secondary Ion Mass Spectrometry (SIMS) data analysis as needed. SIMS measurements of 10-20 samples will be performed at NREL during the project duration.

08 HYDROGEN↗

DOE OSTI2022

Machine learning predictions for local electronic properties of disordered correlated electron systems

We present a scalable machine learning (ML) model to predict local electronic properties such as on-site electron number and double occupation for disordered correlated electron systems. Our approach is based on the locality principle, or the nearsightedness nature, of many-electron systems, which means local electronic properties depend mainly on the immediate environment. A ML model is developed to encode this complex dependence of local quantities on the neighborhood. We demonstrate our approach using the square-lattice Anderson-Hubbard model, which is a paradigmatic system for studying the interplay between Mott transition and Anderson localization. We develop a lattice descriptor based on the group-theoretical method to represent the on-site random potentials within a finite region. The resultant feature variables are used as input to a multilayer fully connected neural network, which is trained from data sets of variational Monte Carlo (VMC) simulations on small systems. We show that the ML predictions agree reasonably well with the VMC data. Our work underscores the promising potential of ML methods for multiscale modeling of correlated electron systems.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Artificial intelligence techniques for integrative structural biology of intrinsically disordered proteins

Here, we outline recent developments in artificial intelligence (AI) and machine learning (ML) techniques for integrative structural biology of intrinsically disordered proteins (IDP) ensembles. IDPs challenge the traditional protein structure-function paradigm by adapting their conformations in response to specific binding partners leading them to mediate diverse, and often complex cellular functions such as biological signaling, self-organization and compartmentalization. Obtaining mechanistic insights into their function can therefore be challenging for traditional structural determination techniques. Often, scientists have to rely on piecemeal evidence drawn from diverse experimental techniques to characterize their functional mechanisms. Multiscale simulations can help bridge critical knowledge gaps about IDP structure-function relationships - however, these techniques also face challenges in resolving emergent phenomena within IDP conformational ensembles. We posit that scalable statistical inference techniques can effectively integrate information gleaned from multiple experimental techniques as well as from simulations, thus providing access to atomistic details of these emergent phenomena.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Laser Powder Directed Energy Deposition of Steels for Nuclear Applications

This comprehensive investigation examines the structure–property relationships in two nuclear alloy systems—Alloy 709 (A709) austenitic stainless steel and Grade 92 (G-92) ferritic/martensitic (F/M) steel—manufactured via directed energy deposition (DED) for sodium-cooled fast reactor applications. This study establishes the fundamental mechanisms for controlling microstructures for optimizing the mechanical performance of additively manufactured nuclear materials through systematic heat treatment optimization and multiscale characterization. As-deposited A709 steel develops a complex multiscale strengthening architecture consisting of a fine cellular solidification structure with diameter of 2-3 µm within10–50 µm grains, elevated dislocation densities from rapid thermal cycling, and grain boundary precipitates that activate concurrent Hall–Petch, dislocation, and precipitation hardening mechanisms to achieve exceptional properties [yield strength (YS): 603 MPa, ultimate tensile strength (UTS): 844 MPa, Vickers hardness: 220 HV] that achieve a 44% superior strength compared to that of the wrought material. Heat treatments produce different results. Solution annealing (SA) dissolves the cellular structure and reduces the hardness to 190 HV. Precipitation treatment (PT) keeps the cellular structure but adds carbides, allowing the hardness to reach 205 HV. The best approach combines both treatments (SA+PT) and creates uniform precipitate distributions with M 23 C 6 carbides at the grain boundaries and MX carbonitrides in the matrix, achieving a hardness of 195 HV. However, directional differences persist, with a 12%–15% strength variation between orientations due to the inherited layered microstructural architecture that survives aggressive heat treatment. While tensile testing at 550°C demonstrates 40%–50% thermal softening with dynamic strain aging, DED A709 steel still maintains a 71% higher YS than that of the wrought material. Ion irradiation studies (100–400 dpa) of DED A709 steel reveal progressive radiation damage with increasing void density and radiation-induced segregation causing nickel enrichment and chromium depletion, which will ultimately compromise mechanical properties. As-deposited G-92 exhibits exceptional strength (UTS: 1650–1700 MPa, 430 HV) through a complex microstructure containing both ferrite and martensite phases, a high geometrically necessary dislocation (GND) density (17.04×10 14 /m 2 ), and fine carbides. Heat treatments create distinct changes. Normalizing produces fresh martensite with the highest hardness (460 HV) and an increased GND density (20.23×10 14 /m 2 ). Tempering develops dual precipitation systems and reduces the hardness to 290 HV. The optimal approach uses sequential normalizing plus tempering, achieving balanced properties with the lowest hardness (250 HV) and a reduced GND density (11.01×10 14 /m 2 ). A processing-dependent anisotropy is observed: horizontal specimens achieve superior ductile behavior, while vertical specimens exhibit brittle failure. A tempering heat treatment successfully mitigates this anisotropic behavior by transforming the hard martensitic as-deposited structure into tempered martensite enabling both horizontal and vertical specimens to exhibit similar stress–strain characteristics with visible necking behavior. Remarkably, testing at 550°C reveals a reversal in the anisotropy, where as-deposited specimens achieve near isotropy with superior thermal stability (a 15%–20% strength reduction), while tempered specimens develop an orientation dependence with a 25%–30% strength reduction. Both alloy systems demonstrate that DED processing creates specimens with a superior strength through refined microstructural features, though with distinct strengthening mechanisms—austenitic through cellular structures and precipitates versus F/M through phase transformations and precipitates. Heat treatment optimization requires alloy-specific approaches, with A709 benefiting from controlled precipitation while G-92 requires careful phase transformation control. The results show that DED manufacturing can produce nuclear materials with exceptional performance, but directional effects and temperature-dependent behavior must be carefully considered for reactor component design and qualification.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2020

Statistical Properties of Magnetic Structures and Energy Dissipation during Turbulent Reconnection in the Earth's Magnetotail

Here, we present the first statistical study of magnetic structures and associated energy dissipation observed during a single period of turbulent magnetic reconnection, by using the in situ measurements of the Magnetospheric Multiscale mission in the Earth's magnetotail on 26 July 2017. The structures are selected by identifying a bipolar signature in the magnetic field and categorized as plasmoids or current sheets via an automated algorithm which examines current density and plasma flow. The size of the plasmoids forms a decaying exponential distribution ranging from subelectron up to ion scales. The presence of substantial number of current sheets is consistent with a physical picture of dynamic production and merging of plasmoids during turbulent reconnection. The magnetic structures are locations of significant energy dissipation via electric field parallel to the local magnetic field, while dissipation via perpendicular electric field dominates outside of the structures. Significant energy also returns from particles to fields.

79 ASTRONOMY AND ASTROPHYSICS↗