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DOE OSTI2020

3D Hybrid Plasmonic Framework with Au Nanopillars Embedded in Nitride Multilayers Integrated on Si

Integration of nanoscale photonic and plasmonic components on Si substrates is a critical step toward Si-based integrated nanophotonic devices. Herein, a set of unique complex 3D metamaterials with intercalated nanolayered and nanopillar structures with tunable plasmonic and optical properties on Si substrates is designed. More specifically, the 3D metamaterials combine metal (Au) nanopillars and alternating metal-nitride (Au-TiN and Au-TaN) nanolayers, epitaxially grown on Si substrates. The ultrafine Au nanopillars ( d ≈ 3 nm) continuously grow throughout all the nanolayers with high epitaxial quality. Novel optical properties, such as highly anisotropic optical property, high absorbance covering the entire visible spectrum regime, and hyperbolic property in the visible regime, are demonstrated. Furthermore, a waveguide based on a silicon nitride (Si 3 N 4 ) ridge with a multilayer structure is successfully fabricated. The demonstration of 3D nanoscale metamaterial design integrated on Si opens up a new route toward tunable metamaterials nanostructure designs with versatile material selection for various optical components in Si integrated photonics.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Ice Phase Classification Made Easy with Score-Based Denoising

Accurate identification of ice phases is essential for understanding various physicochemical phenomena. However, such classification for structures simulated with molecular dynamics is complicated by the complex symmetries of ice polymorphs and thermal fluctuations. For this purpose, both traditional order parameters and data-driven machine learning approaches have been employed, but they often rely on expert intuition, specific geometric information, or large training data sets. In this work, we present an unsupervised phase classification framework that combines a score-based denoiser model with a subsequent model-free classification method to accurately identify ice phases. Further, the denoiser model is trained on perturbed synthetic data of ideal reference structures, eliminating the need for large data sets and labeling efforts. The classification step utilizes the smooth overlap of atomic position (SOAP) descriptors as the atomic fingerprint, ensuring Euclidean symmetries and transferability to various structural systems. Our approach achieves a remarkable 100% accuracy in distinguishing ice phases of test trajectories using only seven ideal reference structures of ice phases as model inputs. This demonstrates the generalizability of the score-based denoiser model in facilitating phase identification for complex molecular systems. The proposed classification strategy can be broadly applied to investigate structural evolution and phase identification for a wide range of materials, offering new insights into the fundamental understanding of water and other complex systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

The Role of Exciton Binding Energy on LO phonon Broadening and Polaron Formation in (BA) 2 PbI 4 Ruddlesden-Popper Films

The effect of carrier–carrier and carrier–phonon interactions is presented in n = 1 (2D) (BA) 2 PbI 4 Ruddlesden–Popper thin films, and their effect is compared to that of conventional MAPbI 3 . While temperature-dependent photoluminescence shows the well-studied structural phase transitions and evidence of longitudinal optical (LO) phonon scattering in MPbI 3 , the 2D (BA) 2 PbI 4 films produce subtler properties. At low temperatures, evidence of two competing intrinsic exciton transitions is observed (P1 and P3), in addition to several extrinsic transitions attributed to the impurities in the (BA) 2 PbI 4 film. At higher temperatures, (BA) 2 PbI 4 is dominated by the two intrinsic excitons that are attributed to varying degrees of localization caused by deformations of the PbI 4 framework mediated by structural relaxation. Although both exciton complexes are well separated from the continuum (490 meV or greater), their interaction with phonons is quite different. In the case of the more strongly bound complex (P3), the strong excitonic nature and short-range interaction are mediated by the emission of LO phonon replicas. In the case of the exciton with the smaller binding energy (P1), the more extended wavefunction manifests itself in strong carrier–phonon scattering and evidence of large, stable polarons.

2D perovskites↗

DOE OSTI2024

Efficient mapping between void shapes and stress fields using Deep Convolutional Neural Networks with sparse data

Establishing fast and accurate structure-to-property relationships is an important component in the design and discovery of advanced materials. Physics-based simulation models like the finite element method (FEM) are often used to predict deformation, stress, and strain fields as a function of material microstructure in material and structural systems. Such models may be computationally expensive and time intensive if the underlying physics of the system is complex. This limits their application to solve inverse design problems and identify structures that maximize performance. In such scenarios, surrogate models are employed to make the forward mapping computationally efficient to evaluate. However, the high dimensionality of the input microstructure and the output field of interest often renders such surrogate models inefficient, especially when dealing with sparse data. Deep convolutional neural network (CNN) based surrogate models have shown great promise in handling such high-dimensional problems. In this paper, a single ellipsoidal void structure under a uniaxial tensile load represented by a linear elastic, high-dimensional and expensive-to-query, FEM model. We consider two deep CNN architectures, a modified convolutional autoencoder framework with a fully connected bottleneck and a UNet CNN, and compare their accuracy in predicting the von Mises stress field for any given input void shape in the FEM model. Additionally, a sensitivity analysis study is performed using the two approaches, where the variation in the prediction accuracy on unseen test data is studied through numerical experiments by varying the number of training samples from 20 to 100.

surrogate modeling; convolutional neural networks;↗

DOE OSTI2026

Model Parameter Development for Complex Materials: Species-Specific Diffusion Barriers in 316 Stainless Steel from Systematic DFT Calculations

Vacancy-mediated diffusion barriers in 316 stainless steel have been systematically calculated using density functional theory to provide essential parameters for mesoscale microstructure evolution models. A statistical sampling approach employing 210 nudged elastic band calculations across multiple special quasi-random structures captures the effects of local chemical environments in this concentrated alloy. The computational methodology addresses challenges specific to chemically disordered systems, including proper magnetic treatment throughout multi-step calculations and validation against experimental structural properties. The calculated activation barriers reveal clear species-dependent diffusion behavior with the hierarchy Ni >> Fe ˜ Cr >> Mo. Nickel exhibits the highest barriers (0.74–1.31 eV, mean 1.045 eV), confirming its role as the slowest-diffusing major component. Iron and chromium show similar moderate barriers averaging 0.587 eV and 0.522 eV, respectively. Remarkably, molybdenum demonstrates exceptionally low barriers (0.12–0.28 eV, mean 0.194 eV), suggesting much higher mobility than previously recognized and potentially significant implications for precipitation kinetics and microstructure evolution. The barrier ranges remain consistent across different 316 SS compositions, supporting parameter transferability for modeling applications. The overall mean barrier of 0.64 eV provides a practical approximation for phase field simulations, while species-specific values enable detailed treatments of diffusion-controlled processes. This systematic approach establishes a validated framework for generating diffusion parameters in other concentrated alloys where experimental data are limited, while providing the first systematic set of species-specific barriers for predictive modeling of 316 stainless steel microstructure evolution.

36 MATERIALS SCIENCE↗

DOE OSTI2020

The Role of Machine Learning in the Understanding and Design of Materials

Developing algorithmic approaches for the rational design and discovery of materials can enable us to systematically find novel materials, which can have huge technological and social impact. However, such rational design requires a holistic perspective over the full multistage design process, which involves exploring immense materials spaces, their properties, and process design and engineering as well as a techno-economic assessment. The complexity of exploring all of these options using conventional scientific approaches seems intractable. Instead, novel tools from the field of machine learning can potentially solve some of our challenges on the way to rational materials design. Here we review some of the chief advancements of these methods and their applications in rational materials design, followed by a discussion on some of the main challenges and opportunities we currently face together with our perspective on the future of rational materials design and discovery.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Simulation methods for liquid–liquid phase separation of disordered proteins

Liquid–liquid phase separation of intrinsically disordered proteins (IDPs) and other biomolecules is a highly complex but robust process used by living systems. Drawing inspiration from biology, phase separating proteins have been successfully utilized for promising applications in fields of materials design and drug delivery. These protein-based materials are advantageous due to the ability to finely tune their stimulus-responsive phase behavior and material properties, and the ability to encode biologically active motifs directly into the sequence. The number of possible protein sequences is virtually endless, which makes sequence-based design a rather daunting task, but also attractive due to the amount of control coming from exploration of this variable space. The use of computational methods in this field of research have come to the aid in several aspects, including interpreting experimental results, identifying important structural features and molecular mechanisms capable of explaining the phase behavior, and ultimately providing predictive frameworks for rational design of protein sequences. We provide an overview of computational studies focused on phase separating biomolecules and the tools that are available to researchers interested in this topic.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2019

Miktoarm Stars via Grafting-Through Copolymerization: Self-Assembly and the Star-to-Bottlebrush Transition

The grafting-through copolymerization of two distinct macromonomers via ring-opening metathesis polymerization is normally used to form statistical or diblock bottlebrush polymers with large total backbone degrees of polymerization (N BB ) relative to that of the side-chains (N SC ). Here, we demonstrate that Grubbs-type chemistry in the opposite limit, namely N BB << N SC , produces well-defined materials with excellent control over ensemble-averaged properties, including molar mass, dispersity, composition, and number of branch points. The dependence of self-assembly on these molecular design parameters was systematically probed using small angle X-ray scattering and self-consistent field theoretic simulations. Moreover, our analysis supports the notion that two-component bottlebrush copolymers with small N BB behave like miktoarm star polymers. The star-to-bottlebrush transition is quantifiable for both statistical and diblock sequences by unique signatures in the experimental scaling of domain spacing and simulated distribution of backbone/side-chain density within lamellar unit cells. These findings represent a conceptual framework that simplifies the synthesis of miktoarm star polymers when dispersity in the number of arms and composition can be tolerated. The analytical approach introduced to distinguish chain conformations in complex macromolecules also complements previous methods, for example form factor scattering and rheology.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2018

Alternate Design Concepts for the High Flux Isotope Reactor using Low-Enriched Uranium Fuel Systems: Scoping Analysis

The Oak Ridge National Laboratory (ORNL) High Flux Isotope Reactor (HFIR) is a multi-mission research reactor dedicated mainly to isotope production, neutron scattering experiments, material irradiation, testing and activation. In the framework of non-proliferation policies, the international community is currently aiming to reduce or even eliminate, when and where possible, the use of Highly Enriched Uranium (HEU: 235 U/U ≥ 20 wt. %) in civilian applications. In that context, most research reactors worldwide still operating with HEU fuel are actively engaged in an effort to convert to Low Enriched Uranium fuel system (LEU: 235 U/U < 20 wt. %). Within the US, the High Performance Research Reactor (USHPRR) fleet (which includes HFIR) is expected to convert to LEU fuel using the UMo “monolithic” fuel system currently under development. ORNL is responsible for the development of a LEU design for HFIR. In the past years, Argonne National Laboratory (ANL) offered technical assistance to the ORNL LEU design team whenever deemed appropriate. Due to their complex nature, acceptable HFIR LEU designs identified so far, all based on the UMo monolithic fuel system, would require a substantial fabrication development effort before being proven commercially viable. In order to mitigate technical risks and reduce cost/schedule uncertainties, it appears appropriate to study alternate design solutions and alternate fuel systems as possible backup options for the HFIR conversion to LEU fuel. In that context, ANL has been exploring alternate design concepts for HFIR. Complementing the ORNL design activities, the goal is to find the best design solution for HFIR in term of cost, performance and safety. The present report summarizes the findings regarding two types of alternate conceptual design: U 3 Si 2 designs and un-contoured UMo design. Based on available tools and methods, preliminary analysis show that both concepts could probably meet performance and steady-state safety requirements but element fabrication would remain complex and would require a non-trivial fabrication development effort.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2014

An Electrically Switchable Metal-Organic Framework

Crystalline metal organic framework (MOF) materials containing interconnected porosity can be chemically modified to promote stimulus-driven (light, magnetic or electric fields) structural transformations that can be used in a number of devices. Innovative research strategies are now focused on understanding the role of chemical bond manipulation to reversibly alter the free volume in such structures of critical importance for electro-catalysis, molecular electronics, energy storage technologies, sensor devices and smart membranes. In this letter, we study the mechanism for which an electrically switchable MOF composed of Cu(TCNQ) (TCNQ = 7,7,8,8-tetracyanoquinodimethane) transitions from a high-resistance state to a conducting state in a reversible fashion by an applied potential. The actual mechanism for this reversible electrical switching is still not understood even though a number of reports are available describing the application of electric-field-induced switching of Cu(TCNQ) in device fabrication.

Fernandez, CA↗

DOE OSTI2017

A holistic view of nucleation and self-assembly

Nucleation is the seminal process in the formation of ordered structures ranging from simple inorganic crystals to macromolecular matrices. Observations over the past 15 years have revealed a rich set of hierarchical nucleation pathways involving higher-order species ranging from multi-ion clusters to dense liquid droplets, as well as transient crystalline or amorphous phases. Despite this complexity, the pathways that lead to nucleation can be described by a holistic framework that is rooted in classical concepts, but which takes into account the coupled effects of perturbations in free-energy landscapes and the impact of dynamical factors. This article describes that framework using a series of in situ transmission electron microscopy and atomic force microscopy studies on inorganic, organic, and macromolecular systems to illustrate the evolution in nucleation processes as these perturbations and dynamical factors come into play. The results provide a common basis for understanding development of order in systems as diverse as simple salt crystals, branched semiconductor nanowires, and microbial membranes.

36 MATERIALS SCIENCE↗

DOE OSTI2019

The effect of co-adsorbed solvent molecules on H 2 binding to metal alkoxides

The introduction of metal alkoxides has been proposed as an attractive option to enhance hydrogen binding energies in porous materials such as metal–organic frameworks (MOFs) for room-temperature hydrogen storage applications. The presence of residual solvent molecules from MOF synthesis can, however, affect the performance of these functional groups. We perform quantum chemical calculations to predict solvent binding energies onto the metal-alkoxides and the temperatures required to drive off the solvent molecules and successfully activate porous materials with these moieties. Calculations are performed for Li, Mg, Zn, Cu, and Ni alkoxides and chloroform (CHCl 3 ), dimethylformamide (DMF), ethanol, methanol, and water solvent molecules. We identify CHCl 3 as a promising solvent that can be removed from these alkoxides at mild temperatures, whereas DMF binds strongly to the metal alkoxides and removal would require temperatures above the present upper bound of thermal stability in MOFs. As a second objective, we calculated the binding energies of hydrogen to metal alkoxide–solvent complexes to explore the effect of any solvent molecules that cannot be removed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2014

Bio-inspired MOF-based Catalysts for Lignin Valorization.

Lignin is a potentially plentiful source of renewable organics, with %7E50Mtons/yr produced by the pulp/paper industry and 200-300 Mtons/yr projected production by a US biofuels industry. This industry must process approximately 1 billion tons of biomass to meet the US Renewable Fuel goals. However, there are currently no efficient processes for converting lignin to value-added chemicals and drop-in fuels. Lignin is therefore an opportunity for production of valuable renewable chemicals, but presents staggering technical and economic challenges due to the quantities of material involved and the strong chemical bonds comprising this polymer. Aggressive chemistries and high temperatures are required to degrade lignin without catalysts. Moreover, chemical non-uniformity among lignins leads to complex product mixtures that tend to repolymerize. Conventional petrochemical approaches (pyrolysis, catalytic cracking, gasification) are energy intensive (400-800 degC), require complicated separations, and remove valuable chemical functionality. Low-temperature (25-200 degC) alternatives are clearly desirable, but enzymes are thermally fragile and incompatible with liquid organic compounds, making them impractical for large-scale biorefining. Alternatively, homogeneous catalysts, such as recently developed vanadium complexes, must be separated from product mixtures, while many heterogenous catalysts involve costly noble metals. The objective of this project is to demonstrate proof of concept that an entirely new class of biomimetic, efficient, and industrially robust synthetic catalysts based on nanoporous Metal- Organic Frameworks (MOFs) can be developed. Although catalytic MOFs are known, catalysis of bond cleavage reactions needed for lignin degradation is completely unexplored. Thus, fundamental research is required that industry and most sponsoring agencies are currently unwilling to undertake. We introduce MOFs infiltrated with titanium and nickel species as catalysts for the C-O bond hydrogenolysis in model compounds, which mimic the b-O-4, a-O-4, and 4-O-5 linkages of natural lignin. The versatile IRMOF-74(n) series is proposed as a platform for creating efficient hydrogenolysis catalysts as it not only displays tunable pore sizes, but also has the required thermal and chemical stability. The catalytic C-O bond cleavage occurs at 10 bar hydrogen pressure and temperatures as low as 120 degC. The conversion efficiency of the aromatic ether substrates into the corresponding hydrocarbons and phenols varies as PhCH 2 CH 2 OPh > PhCH 2 OPh > PhOPh (Ph = phenyl), while the catalytic activity generally follows the following trend Ni@IRMOF-74>Ti@IRMOF-74>IRMOF-74. Conversions as high as 80%, coupled with good selectivity for hydrogenolysis vs. hydrogenation, highlight the potential of MOF-based catalysts for the selective cleavage of recalcitrant aryl-ether bonds found in lignin and other biopolymers. This project supports the DOE Integrated Biorefinery Program goals, the objective of which is to convert biomass to fuels and high-value chemicals, by addressing an important technology gap: the lack of low-temperature catalysts suitable for industrial lignin degradation. Biomass, which is %7E30 wt% lignin, constitutes a potentially major source of platform chemicals that could improve overall profitability and productivity of all energy-related products, thereby benefiting consumers and reducing national dependence on imported oil. Additionally, DoD has a strong interest in low-cost drop-in fuels (Navy Biofuel Initiative) and has signed a Memorandum of Understanding with DOE and USDA to develop a sustainable biofuels industry.

Allendorf, Mark D.↗

DOE OSTI2022

Examining polymer‐protein biophysical interactions with small‐angle x‐ray scattering and quartz crystal microbalance with dissipation

Abstract Polymer‐protein hybrids can be deployed to improve protein solubility and stability in denaturing environments. While previous work used robotics and active machine learning to inform new designs, further biophysical information is required to ascertain structure–function behavior. Here, we show the value of tandem small‐angle x‐ray scattering (SAXS) and quartz crystal microbalance with dissipation (QCMD) experiments to reveal detailed polymer‐protein interactions with horseradish peroxidase (HRP) as a test case. Of particular interest was the process of polymer‐protein complex formation under thermal stress whereby SAXS monitors formation in solution while QCMD follows these dynamics at an interface. The radius of gyration ( R g ) of the protein as measured by SAXS does not change significantly in the presence of polymer under denaturing conditions, but thickness and dissipation changes were observed in QCMD data. SAXS data with and without thermal stress were utilized to create bead models of the potential complexes and denatured enzyme, and each model fit provided insight into the degree of interactions. Additionally, QCMD data demonstrated that HRP deforms by spreading upon surface adsorption at low concentration as shown by longer adsorption times and smaller frequency shifts. In contrast, thermally stressed and highly inactive HRP had faster adsorption kinetics. The combination of SAXS and QCMD serves as a framework for biophysical characterization of interactions between proteins and polymers which could be useful in designing polymer‐protein hybrids.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2023

A lightweight Fe–Mn–Al–C austenitic steel with ultra-high strength and ductility fabricated via laser powder bed fusion

Lightweight Fe–Mn–Al–C steels have become a topic of significant interest for the defense and automotive industries. These alloys can maintain high strength and ductility while also reducing weight in structural applications. Conventionally processed Fe–Mn–Al–C austenitic steels with high Al content (~9 wt%) demonstrate greater than 1.5 GPa strength with 35% elongation. Several recent studies have demonstrated success in fabricating steel parts using laser powder bed fusion (L-PBF) additive manufacturing (AM), which can generate near-net-shape components with complex geometries and is capable of local microstructural control. However, studies on L-PBF processing of Fe–Mn–Al–C alloys have focused on low Al content (<5 wt%) compositional regimes representing alloys that undergo transformation-induced plasticity (TRIP) and twinning-induced plasticity (TWIP). Here, in this study, we present the effects of L-PBF processing on the microstructure and mechanical properties of an Fe–30Mn–9Al–1Si-0.5Mo-0.9C austenitic steel. A process optimization framework is employed to determine an ideal L-PBF processing space that will result in >99% density parts. Implementing this framework resulted in near-fully dense specimens fabricated over a broad range of process parameters. Additionally, two bi-directional scan rotation strategies (90° and 67°) were applied to understand their effects on texture and anisotropy in this material. As-printed specimens displayed considerable work-hardening characteristics with average strengths of up to 1.3 GPa and 36% elongation in the build direction. However, solidification microcracks oriented in the build direction resulted in anisotropy in tensile strength and ductility resulting in average strengths of 1.1 GPa and 20% elongation perpendicular to the build direction. The successful L-PBF fabrication of Fe–30Mn–9Al–1Si-0.5Mo-0.9C presented here is expected to open new avenues for weight reduction in structural applications with a high degree of control over part topology.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Development of a Reference Governor-based Control Scheme for Integrated Energy Systems

The concept of an Integrated Energy System (IES) is meant to combine different energy technologies in synergistic ways to achieve a more secure and economical energy supply. The RAVEN-based HYBRID framework is used to find the optimal installed capacity and the optimal economical dispatch of each component of the IES. In practice what is achievable is limited by not only the inherent physical response characteristics of individual plant units but also by equipment operating limits. The inherent response characteristics are presently accounted for in the RAVEN-based HYBRID framework by dynamic models for plant units. The equipment operating limits, which are not yet addressed in HYBRID, are the subject of this report. These limits are set to achieve a design service life for a plant unit and are based on material degradation in the presence of mechanical fatigue induced by thermal cycling and by structure creep resulting from elevated temperature conditions. In general, the plant unit designer determines and specifies the allowable operating range of key process variables. It is then up to the integrated system design engineer to ensure those conditions are met. This can be accomplished through a combination of limiting the demand rates made on the plant unit and designing a control system that limits variations of key process variables. The typical control system design procedure, however, is not well suited for achieving this for complex dynamic systems such as encountered in integrated energy systems where multiple plant units must be simultaneously coordinated. This report describes how equipment operating limitations in an IES can be formally met through a control system design procedure know as the Reference Governor. The approach augments the existing controllers in a plant unit with an advance controller that modifies the issued setpoints so that selected process variables remain within limits. The design procedure is applied to the reference multi-unit system that has been the focus of Modeling and Simulation task in the Integrated Energy System program. Results describing application to the problem of limiting key process variables during operational transients for two of the plant units, the balance of plant and the gas turbine, are then presented. It is demonstrated that the Reference Governor method is an effective means for ensuring equipment operating constraints can be met in an IES. The software that implements the method has been made accessible to others on the Modeling and Simulation upload site.

42 ENGINEERING↗

DOE OSTI2025

Advancing Additive Manufacturing Through Artificial Intelligence–Powered, High-Throughput, Nondestructive Characterization and Process Optimization

This Cooperative Research and Development Agreement (CRADA) between Oak Ridge National Laboratory (ORNL) and ZEISS Industrial Metrology has demonstrated the transformative potential of artificial intelligence (AI)-enabled x-ray computed tomography (XCT) to accelerate the qualification and certification of additively manufactured (AM) parts. At the core of this effort is Simurgh, an AI-powered XCT reconstruction framework jointly advanced by ORNL and ZEISS that integrates computer-aided design (CAD) models, physics-based simulations, and deep learning to overcome the long-standing challenges of metal artifact correction, long scan durations, and limited flaw detectability in dense and geometrically complex components. Simurgh enables high-throughput, high-quality 3D reconstruction from sparse and fast scans, which reduces XCT acquisition times by more than an order of magnitude and simultaneously improves defect detection limits by up to fourfold compared with industry-standard approaches. This capability reduces scan costs by more than 50%, lowers labor overhead, and makes XCT characterization economically viable for routine industrial use. By enabling reliable flaw detection in minutes rather than hours, Simurgh facilitates real-time feedback loops for process parameter optimization, which was highlighted in a recent npj Computational Materials (a Nature journal) issue. In the published study, more than 100 alloy coupons were characterized within a single day. This work represents a tenfold acceleration in the development of novel AM alloys and processes compared with conventional workflows. The ZEISS collaboration has also demonstrated the scalability of Simurgh to diverse application domains, including aerospace, nuclear, automotive, and biomedical components; in these applications, ensuring structural integrity is paramount. By drastically reducing barriers to XCT adoption, this partnership has laid the foundation for digital twins and data-driven certification pipelines and directly addressed bottlenecks in qualifying new materials and designs. Together, ORNL and ZEISS have shown that Simurgh advances the state of the art in nondestructive evaluation and aligns with the broader mission of enabling Industry 4.0 manufacturing ecosystems, in which intelligent, cost-effective, rapid quality assurance is integral to accelerating innovation and ensuring safety in critical applications.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Advancing Additive Manufacturing Through Artificial Intelligence–Powered, High-Throughput, Nondestructive Characterization and Process Optimization

36 MATERIALS SCIENCE↗