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At least 199 records · Page 11

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DOE OSTI2025

Accelerating phase field simulations through a hybrid adaptive Fourier neural operator with U-net backbone

Prolonged contact between a corrosive liquid and metal alloys can cause progressive dealloying. For one such process as liquid-metal dealloying (LMD), phase field models have been developed to understand the mechanisms leading to complex morphologies. However, the LMD governing equations in these models often involve coupled non-linear partial differential equations (PDE), which are challenging to solve numerically. In particular, numerical stiffness in the PDEs requires an extremely refined time step size (on the order of 10 -12 s or smaller). This computational bottleneck is especially problematic when running LMD simulation until a late time horizon is required. This motivates the development of surrogate models capable of leaping forward in time, by skipping several consecutive time steps at-once. In this paper, we propose a U-shaped adaptive Fourier neural operator (U-AFNO), a machine learning (ML) based model inspired by recent advances in neural operator learning. U-AFNO employs U-Nets for extracting and reconstructing local features within the physical fields, and passes the latent space through a vision transformer (ViT) implemented in the Fourier space (AFNO). We use U-AFNOs to learn the dynamics of mapping the field at a current time step into a later time step. We also identify global quantities of interest (QoI) describing the corrosion process (e.g., the deformation of the liquid-metal interface, lost metal, etc.) and show that our proposed U-AFNO model is able to accurately predict the field dynamics, in spite of the chaotic nature of LMD. Most notably, our model reproduces the key microstructure statistics and QoIs with a level of accuracy on par with the high-fidelity numerical solver, while achieving a significant 11, 200 × speed-up on a high-resolution grid when comparing the computational expense per time step. Finally, we also investigate the opportunity of using hybrid simulations, in which we alternate forward leaps in time using the U-AFNO with high-fidelity time stepping. We demonstrate that while advantageous for some surrogate model design choices, our proposed U-AFNO model in fully auto-regressive settings consistently outperforms hybrid schemes.

36 MATERIALS SCIENCE↗

DOE OSTI2025

High-throughput synthesis of high-entropy alloys via parallelized electric field assisted sintering

Materials discovery and design is an expensive and time-consuming process, though necessary to advance many engineering fields. In this work, a novel tooling design is utilized in conjunction with electric field assisted sintering (EFAS) to effectively create a new high-throughput synthesis technique: parallelized EFAS. Through this technique, a wide range of material compositions and geometries can be synthesized in parallel as isolated samples or as part of contiguous arrays. Multiple tooling designs are explored to examine both the flexibility and limitations of the technique. A series of increasing complex alloys is produced simultaneously using in situ alloying, beginning with pure Ni and adding equimolar constituents up to the septenary high-entropy alloy AlCoCrCuFeMnNi. Microstructural characterization reveals each sample is effectively fully dense and chemically homogenous while exhibiting phases in agreement with CALPHAD predictions. Scalability of parallelized EFAS is then experimentally demonstrated and the implications for materials discovery and automation are discussed.

36 - MATERIALS SCIENCE↗

DOE OSTI2024

Impact of temperature variations on BISON predictions of Ag release in AGR-1 and AGR-2 experiments

Understanding and quantifying the release of fission products like silver (Ag) from TRistructural ISOtropic (TRISO) fuel particles is important to assess the safe operation of advanced high temperature reactors. Although the silicon carbide (SiC) layer of TRISO particles is effective as the main fission product barrier, Ag can be released from intact TRISO particles. A mechanistic model for the effective Ag diffusivity, D eff , was previously developed as a function of temperature and microstructure variables informed by atomistic modeling of Ag diffusivity on the mesoscale. Here in this study, we use this model to explore how experimental temperature uncertainties impact the overall predicted Ag release. This analysis shows that temperature uncertainties have a significant impact on the overall Ag release predictions. Furthermore, we show that the time average volume average temperature (TAVA) temperature is not an appropriate proxy for temperature histories to predict fission product release. We attribute this to the Arrhenius dependence of Ag diffusivity with respect to temperature. The detailed temperature histories, therefore, provide the most accurate results are are of most importance for modeling efforts. Overall, this work shows the importance of considering the experimental uncertainty of the temperature on computational predictions of fission product transport and release and the need for more accurate temperature histories from future experiments.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2026

Deposition Height Prediction in Directed Energy Deposition

Using 316L stainless steel as a model material, reduced-order models are developed to predict capture efficiency, deposition height, and site-specific hardness in directed energy deposition. Capture efficiency is predicted over a 15 to 55 pct range using a dimensionless number derived from processing conditions and thermophysical properties. Deposition height is predicted over a 0.3 to 1.3 mm range without in situ sensing or prior training data, using two models based on the same mass and energy-balance principles. Predictions are compared with machine learning approaches. A quantitative relationship links deposition height, primary dendrite arm spacing (PDAS), and hardness: heights of 0.3 to 1.1 mm correspond to PDAS values of 2.7 to 5.1 µm and Vickers hardness (HV) of 160 to 219. Thinner layers cool more rapidly, producing finer microstructures and higher hardness. Samples fabricated with in situ variations in deposition height exhibited up to 55 HV differences between thick and thin regions, demonstrating that local control of deposition height enables predictive, site-specific hardness within a single build. These results establish deposition height prediction as a pathway for a priori process design and property control in directed energy deposition for 316L stainless steel.

Kunkel, William [Univ. of Wisconsin, Madison, WI (↗

DOE OSTI2024

Bayesian Analysis of TRISO Fuel: Quantifying Model Inadequacy, Incorporating Lower-Length-Scale Effects, and Developing Parallel Active Learning Capabilities

The U.S. Department of Energy (DOE)’s Nuclear Energy Advanced Modeling and Simulation (NEAMS) program aims to develop predictive capabilities by applying computational methods to the analysis and design of advanced reactor and fuel-cycle systems. This program has been providing engineering-scale support for the continued development of BISON, a high-fidelity, high-resolution fuel performance tool. Fuel behavior in nuclear reactors is governed by a complex network of mechanisms that interact with various other physics aspects in the reactor system. Any model developed to represent fuel behavior will likely be idealized, resulting in uncertainties when comparing their predictions against the observed data. In Fiscal Year (FY)-23, we initiated the Uncertainty Quantification (UQ) work by using Bayesian methods to establish a level of model trustworthiness and further improve it, with a particular emphasis on TRI-Structural isOtropic (TRISO) nuclear fuel. This year, we further expanded on that UQ work by investigating an approach to quantifying model inadequacy and accounting for lower-length scale (LLS) effects in TRISO silver (Ag) release modeling. Furthermore, we are implementing parallel active learning capabilities to reduce the computational cost (i.e., required computational resources and elapsed time) of performing UQ. Specifically, we utilized The Kennedy O’Hagan framework for Bayesian uncertainty quantification (KOH) to account for model inadequacy in TRISO Ag release predictions made by BISON. The KOH framework represents an improvement over the standard Bayesian framework used in FY-23. Explicitly accounting for model inadequacy in the Bayesian framework helps establish the level of experimental noise uncertainty in the Advanced Gas Reactor (AGR) data. We compared the inverse UQ results obtained from both the standard Bayesian and KOH frameworks in light of the AGR-2/3/4 data, and also compared the predictive UQ results obtained from these two frameworks in light of the AGR-1 data. Next, we investigated the impact of considering LLS effects in the Ag release simulations. We developed an expanded database of LLS simulated effective diffusivities for Ag, covering a wide range of microstructures and temperatures. Using this database, we developed a framework for incorporating LLS effects into the engineering-scale Ag release UQ. We developed both parametric and non-parametric approaches for bridging the length scales. We then investigated the inverse UQ results in light of the AGR-2/3/4 data and the predictive UQ results in light of the AGR-1 data, and compared the LLS-informed approach and the Arrhenius equation, which does not include microstructure information. Finally, we discussed implementing parallel active learning capabilities in the Multiphysics Object Oriented Simulation Environment (MOOSE)/BISON to reduce the computational cost (i.e., computational resources and elapsed time) of Bayesian UQ. For verification purposes, we first tested these new capabil ities on a species interaction problem. We then demonstrated them on the TRISO Ag release application, showing that parallel active learning capabilities can enhance the accuracy of UQ while also substantially reducing the computational cost in comparison to the reference methods developed in FY-23.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2024

Anomaly Detection in Materials Digital Twins with Multiscale ICME for Additive Manufacturing

Detecting anomaly in fatigue and fracture experimental materials science is an interesting yet challenging topic. The reasons are threefold. First, the anomalous microstructure feature that gives rise to structural failure is small, sometimes in the order of 10 -7 of the interrogated volume. This, in turn, results in a highly imbalanced classification problem in machine learning (ML). Second, the consequence is high, in the sense that the test specimen is destructed in such case. Third, the convolution between microstructure stochasticity and the small probability of void nucleation, growth, and coalescence makes failure and fracture a hard-to-predict and challenging problem in materials science due to its irreproducibility, even experimentally. In this paper, we developed a materials digital twin and applied anomaly detection methods to detect voids and anomaly in additive manufacturing (AM). The materials digital twin is driven by two integrated computational materials engineering (ICME) models, which are kinetic Monte Carlo (kMC) and crystal plasticity finite element method (CPFEM). In conclusion, we demonstrated that by using anomaly detection, it is possible to detect voids and other defects in materials digital twin, which paves way for future research in integrating materials digital twin with its physical counterpart.

ICME↗

DOE OSTI2025

The role of specimen size and grain boundary characteristics in the yield strength of tungsten in microtensile tests

To effectively use the measured properties from small-scale tensile tests for bulk material performance predictions, it is essential to understand the threshold of specimen size-effect strengthening and the interaction between dislocations and microstructures within miniaturized specimens. This study uses pure tungsten to investigate the size effect in terms of specimen size, grain size, and grain boundary characteristics relative to the yield strength of tungsten at room temperature. We evaluate the transition from miniaturized specimen properties to bulk properties and the deformation behavior through small-scale tensile tests of three specimen sizes (large: 80 × 100 × 233 µm³; medium: 7 × 7 × 18 µm³; and small: 2 × 2 × 5 µm³). The testing results reveal that the small and medium specimens exhibit high yield strength with ductile behavior, while the large specimens exhibit brittle failure, consistent with the room temperature strength of tungsten, indicating bulk behavior. We further explore the specimen size-effect sensitivity to yield stress and the scaling relationship between yield strength and the number of grains involved in the deformation. A power-law relationship with the exponent value of approximately -0.5 was found in the yield strength–grain number scaling, implying the Hall-Petch like behavior. A minimum of 7–17 effective grain boundaries across the tensile gauge dimension is required to accurately measure bulk properties.

36 - MATERIALS SCIENCE↗

DOE OSTI2025

Modulation of thermal conductivity of iron-doped ß-Ga2O3 by helium-ion irradiation

This study examines the impact of helium-ion irradiation on the thermal conductivity of ß-Ga2O3. A laser-based spatial domain thermoreflectance technique is used to investigate thermal conductivity map for both un-irradiated and irradiated ß-Ga2O3, which are then validated against simulation results derived from density functional theory-based phonon transport simulations. Since helium bubble evolution was ob- served at the nanoscale using transmission electron microscopy, the simulation study was carried out on eight distinct helium-induced sites in ß-Ga2O3. Our findings indicate a reduction in thermal conductivity for the irradiated samples. Experimental results show a significant reduction in thermal conductivity in irradiated samples, with de- creases of approximately 25% along the [100] direction and 40% along [001] directions. Phonon transport simulations closely replicate these findings, particularly when helium occupying interstitial sites, predicting reductions of ˜53% along [100] and ˜50% along [001] directions. This work underscores the role of irradiation-induced microstructural changes in the heat transport properties of ß-Ga2O3 which is crucial for its application in sensor devices in extreme environments.

36 - MATERIALS SCIENCE↗

DOE OSTI2026

The structure, composition, and performance impact of a YSZ-GDC interdiffusion layer in solid oxide electrolysis cells

This study provides a combined experimental and computational investigation into the structure and impact of the cation interdiffusion layer that appears at the gadolinium doped ceria (GDC)/yttria stabilized zirconia (YSZ) interface in solid oxide electrolysis cells (SOECs). Scanning transmission electron microscopy (STEM) illustrates that a ∼0.4 μm interdiffusion layer (IDL) with an intermixed cation distribution and fine grain size forms upon sintering. STEM identifies that the interdiffusion layer exists in the cubic fluorite structure despite changes in cation composition. The interdiffusion layer microstructure formed during sintering does not change during SOEC testing at either 1.3V or heightened voltage pulse testing. Modeling predicts that ionic conductivity may decrease in the interdiffusion layer due to Coulombic trapping between mobile oxygen vacancies and excess Gd 3+ acceptor dopants. Yet, the density and continuous nature of the layer should benefit cell stability by substantially reducing the formation of SrZrO 3 , which is corroborated by STEM and Synchrotron X-ray diffraction (XRD). We conclude that the interdiffusion layer acts as a beneficial barrier to Sr diffusion, when operating in a regime where electrolyte void formation is not observed.

organic↗

DOE OSTI2026

Operando neutron radiography validates a parameter-free transport–kinetics model for thick solid-state battery cathodes

Tortuosity-weighted interfacial flux for lithium (TWIF-Li) predicts through-thickness Li gradients in thick composite all-solid-state cathodes without fitted parameters. Image-derived microstructures, GITT-derived concentration-dependent solid diffusion, and tortuosity-weighted interfacial kinetics reproduce operando neutron radiography across practical rates, delivering transferable design rules to suppress transport-limited reaction fronts.

Adam, Andre [ORNL] (ORCID:0000000245023033)↗

DOE OSTI2026

Influence of fabrication on microstructure and heat affected zone width in weldments of nuclear reactor pressure vessel steel

Advanced manufacturing routes such as electron beam welding and powder metallurgy with hot isostatic pressing are increasingly used across energy and aerospace industries, where the reliable prediction of weld behavior and heat affected zone (HAZ) evolution is critical. This study examines how fabrication routes and post-weld heat treatments influence phase distribution, crystallite size, microstrain, and dislocation density in nuclear reactor pressure vessel steels using synchrotron X-ray diffraction (SXRD). Retained austenite occurs only in samples that did not undergo austenitization, whereas an austenitizing heat treatment fully eliminates retained austenite and produces a more uniform microstructure across the weldment in terms of phase fraction, dislocation density, and microstrain. The Rosenthal solution underestimates the HAZ width for powder metallurgy samples. A newly proposed modified Rosenthal solution, reducing density by accounting for porosity, matches the SXRD-measured HAZ width with a 0.65% error. Structure–property correlations reveal that dislocation density correlates strongly with nanohardness in homogenous microstructures, while in heterogenous weldments nanohardness is further influenced by the presence of dissimilar phase boundaries. These findings provide new insight into the thermal and microstructural response of powder metallurgy fabricated steels and offer a framework for optimizing welding procedures and heat treatments in advanced manufacturing applications.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

Mesoscale Modeling Approach for Quantifying Microstructure-Aware Micromechanical Responses in Metal Hydrides

Metal hydrides can undergo significant volume changes upon hydrogen uptake and release, which induce a mechanical response that depends not only on the evolving hydrogen composition but also on the microstructure. We present a comprehensive mesoscale modeling framework based on microelasticity theory to quantify the micromechanical responses of metal hydrides, specifically focusing on a hydrogenating polycrystalline MgH 2x particle within a host material as a model micromechanical system. Utilizing digitally generated realistic microstructures and density-functional-theory-derived parameters, we analyzed highly nonuniform local stress profiles in the polycrystalline hydrides under the clamping force exerted by the host during hydrogenation. Our framework also allows us to predict the corresponding strain energy accumulation and mechanical hot spots formation in the hydrides, highlighting their roles in thermodynamic destabilization and mechanical failure, respectively. Through extensive parametric simulations, we further quantified the influence of interface type, crystallinity, grain size, loading ratio, and host stiffness, providing practical guidance for optimizing microstructural design and host material selection. This proposed approach is broadly applicable to micromechanical systems with complex microstructural features involving chemical reaction- and/or phase-transformation-induced deformation.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Microstructure development during rapid alloy solidification

Abstract Solidification processing of structural alloys can take place over an extremely wide range of solid–liquid interface velocities spanning six orders of magnitude, from the low-velocity constitutional supercooling limit of microns/s to the high-velocity absolute stability limit of m/s. In between these two limits, the solid–liquid interface is morphologically unstable and typically forms cellular-dendritic microstructures, but also other microstructures that remain elusive. Rapid developments in additive manufacturing have renewed the interest in modeling the high-velocity range, where approximate analytical theories provide limited predictions. In this article, we discuss recent advances in phase-field modeling of rapid solidification of metallic alloys, including a brief description of state-of-the-art experiments used for model validation. We describe how phase-field models can cope with the dual challenge of carrying out simulations on experimentally relevant length- and time scales and incorporating nonequilibrium effects at the solid–liquid interface that become dominant at rapid rates. We present selected results, illustrating how phase-field simulations have yielded unprecedented insights into high-velocity interface dynamics, shedding new light on both the absolute stability limit and the formation of banded microstructures that are a hallmark of rapid alloy solidification near this limit. We also discuss state-of-the-art experiments used to validate those insights. Graphical abstract

36 MATERIALS SCIENCE↗

DOE OSTI2025

Seeing is Believing: Autonomous Microscopy and the Data Revolution in Materials Science

This presentation explores the transformative potential of autonomous electron microscopy and artificial intelligence (AI) in accelerating materials science discovery, particularly for energy applications and materials operating in extreme environments. We discuss pioneering self-driving laboratories at NREL designed to intelligently probe material synthesis and degradation across multiple scales, aiming to rapidly bridge the gap between atomic-level understanding and the development of high-performance, reliable materials. Utilizing advanced machine learning techniques, such as few-shot learning and multimodal analysis integrating imaging and spectroscopy, we demonstrate methods to extract actionable descriptors for material behavior, quantify complex microstructural evolution, and statistically link synthesis parameters to defect populations. This AI-driven approach promises to accelerate the creation of predictive materials tailored for specific missions, enabling faster development cycles and enhanced material assurance.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Seeing is Believing: Autonomous Microscopy and the Data Revolution in Materials Science

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Which way does the dendrite grow? Competition among epitaxy, preferred growth direction, and thermal gradients in powder bed fusion additive manufacturing

The as-processed microstructure of metal alloy parts manufactured through laser powder bed fusion (LPBF) is heavily derived from the cellular dendritic solidification. The growth direction of dendrites within the melt pool is determined through competition among epitaxial growth, preferred growth directions, and maximum thermal gradients. However, the dominant factor and the specific role of each in developing melt pool microstructures remain unknown. Here, in this study, we performed single laser track scans on an SS316L single crystal substrate and combined experimental characterization of microstructure and crystal orientations with Computational Fluid Dynamics simulations of thermal gradients to evaluate the role of each factor in determining dendritic growth direction and evolution. Our results reveal that epitaxial growth dominates microstructure development by preferentially growing along a single 〈100〉 variant of the single crystal substrate adjacent to the melt pool boundary. Under LPBF’s highly curved and rapidly evolving thermal field, this preferential dendrite variant selection and its continued growth from the melt pool boundary to the centerline are governed by the local temperature gradient magnitude at the solid-liquid interface, rather than by the instantaneous maximum temperature gradient direction alone. Using these findings, we successfully predict changes in the dendrite growth direction with changing laser scan direction on a single crystal substrate, and show that the geometric melt pool centerline can deviate from the microstructural centerline because asymmetric local temperature gradient magnitudes transiently limit growth, resulting in different dendrite travel distances on each side of the melt pool.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Anisotropic physics-regularized interpretable machine learning of microstructure evolution

Anisotropic Physics-Regularized Interpretable Machine Learning Microstructure Evolution (APRIMME) is a general-purpose machine learning solution for grain growth simulations. In prior work, PRIMME employed a deep neural network to predict site-specific migration as a function of its neighboring sites to model normal, isotropic, grain growth behavior. This work aims to extend this method by incorporating grain boundary misorientation-based grain growth behavior. APRIMME is trained on anisotropic simulations created using the Monte Carlo-Potts (MCP) model. Furthermore, the results of this work are compared statistically using grain radius, number of sides per grain, mean neighborhood misorientations, and the standard deviation of triple junction dihedral angles, and are found to match in most cases. The exceptions are small and seem to be related to two causes: (1) the deterministic model of APRIMME is learning from the stochastic simulations of MCP, which seems to accentuate triple junction behaviors; and, (2) a bias against very small grains is made evident in a quicker decrease in grains than expected at the beginning of an APRIMME simulation. APRIMME is also evaluated for its general ability to capture anisotropic grain growth behavior by first investigating different test case initial conditions, including a circle grain, three grain, and hexagonal grain microstructures.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Process-driven roadmap for depositing super duplex stainless steel via wire Arc additive manufacturing

Here, this study systematically investigates the effects of shielding gas, bead spacing, weld mode, and travel speed on the phase balance, porosity, and hardness of wire arc additively manufactured (WAAM) super duplex stainless steel (ER2594). Robotic WAAM was employed to fabricate multilayer walls under systematically varied process conditions, followed by phase transformation simulations, X-ray computed tomography (XCT), electron backscatter diffraction (EBSD), and microhardness evaluation. Thermodynamic simulations predicted rapid cooling of AM process can suppress the potential formation of deleterious precipitates which was later validated via cross-sectional microstructure analyses of printed samples. XCT revealed porosity levels below 0.2% for all deposits, with reduced travel speed significantly lowering defect volume. Microstructural analyses revealed the evolution of various austenite precipitates, including grain boundary austenite (GBA), Widmanstätten austenite (WA), and intergranular austenite (IGA), sequentially upon cooling of the ferrite phase. Among all process parameters, weld transfer mode exhibited the strongest influence on phase balance; pulsed mode promoted higher ferrite retention (~ 36%) compared to RapidX mode. No consistent relationship between stabilized phase fraction and captured microhardness was observed. This work provides critical insights for optimizing WAAM parameters to control phase balance and mechanical performance, which is essential for producing high-integrity super duplex stainless-steel components for nuclear and marine applications.

Grain orientation↗