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DOE OSTI2024

Improving Additive Manufactured Component Performance through Multi-Scale Microstructure Simulation and Process Optimization

The purpose of this project was to utilize computational tools to understand the relationships between processing, microstructure, and properties for additively manufactured (AM) aluminum alloys for automotive applications, and to provide an engineering solution for helping to optimize process conditions. The project leverages ORNL developments in computational modeling, including AM process modeling, phase-field based microstructure evolution predictions, and data analytics techniques for mapping process conditions to material outcomes. The project utilized an Al-Cu-Mn-Zr alloy as a model material for studying formation of defects and microstructural features in response to variations in process conditions. Based on both pre-existing experimental data and simulation results, statistical process maps were constructed to identify regions of process space with minimal defect formation and advantageous microstructures and properties. The software tools used for this purpose were successful disseminated to GM, who were able to successful compile the relevant HPC codes within their own computing ecosystem and perform initial calculations to reproduce ORNL results.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Impact of thermodynamic fluctuations and pattern size on the nucleation behavior during area selective deposition

In this work, the authors explore the impact that thermodynamic fluctuations have on the spontaneous appearance of defects during area selective deposition on patterned surfaces that are fully covered by adsorbates under local thermodynamic equilibrium, such as self-assembled monolayers. By using a simple lattice gas model for the adsorbed monolayer, the authors were able to track the spontaneous formation of defects in the monolayer as a function of the pattern width. The results indicate that, for pattern widths of the order of tens of nanometers, roughening effects at the pattern edge can be the leading source for the spontaneous appearance of nucleation defects. This leads to an enhancement of the density of defects that can be up to three orders of magnitude higher than those expected in uniform (not patterned) surfaces. The model also predicts a density of defects that is inversely proportional to the pattern width. Finally, if the dynamic nucleation of defects during area selective deposition is driven by thermodynamic fluctuations, the model predicts that the nucleation rate should be proportional to the total precursor fluency and independent of purge times. Moreover, a tight confinement of the monolayer through a high quality smooth interface in the patterned substrate and strong cohesive interactions between adsorbates should each contribute to a reduction of the overall defect density.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Aging Mechanisms of Broad Area ~800 nm Laser Diodes

Here, this work presents a comprehensive study of early aging behavior (<500 hr) in ~800 nm, phosphide-based laser diodes grown by solid-source MBE with different oxygen concentration levels incorporated into the diode epitaxial layers during growth. The data indicate that lasing characteristics prior to aging are degraded by oxygen introduction, but the gradual power degradation rate after the onset of aging is not a strong function of oxygen at these concentration levels. Devices with oxygen concentrations of ~2.5 × 10 15 cm -3 showed significantly longer delay before the onset of aging (incubation time) than devices with less than 1 × 10 15 cm -3 oxygen. Generation-Recombination current and Laser Beam Induced Current measurements indicate that defect densities and aggregation are suppressed at the facets by oxygen, which can explain longer incubation times. Diagnostic data and parametric fits to diode simulation models show that increased cavity optical loss and defect density are primarily responsible for gradual power degradation during aging, rather than changes in nonradiative recombination. Mechanisms are proposed that explain this behavior, based on density functional theory (DFT) simulations and known recombination-enhanced defect generation phenomena.

47 OTHER INSTRUMENTATION↗

DOE OSTI2024

Additive manufacturing of defect-free TiZrNbTa refractory high-entropy alloy with enhanced elastic isotropy via in-situ alloying of elemental powders

Laser powder-bed fusion (L-PBF) additive manufacturing presents ample opportunities to produce net-shape parts. The complex laser-powder interactions result in high cooling rates that often lead to unique microstructures and excellent mechanical properties. Refractory high-entropy alloys show great potential for high-temperature applications but are notoriously difficult to process by additive processes due to their sensitivity to cracking and defects, such as un-melted powders and keyholes. Here, we present a method based on a normalized model-based processing diagram to achieve a nearly defect-free TiZrNbTa alloy via in-situ alloying of elemental powders during L-PBF. Compared to its as-cast counterpart, the as-printed TiZrNbTa exhibits comparable mechanical properties but with enhanced elastic isotropy. This method has good potential for other refractory alloy systems based on in-situ alloying of elemental powders, thereby creating new opportunities to rapidly expand the collection of processable refractory materials via L-PBF.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Modeling of flash sintering of ionic ceramics

A fundamental understanding of the influence of defects in ionic ceramics at the atomic, microstructural, and macroscopic levels, before, during, and after the flash sintering event is key to the development of ceramic processing operations that lead to fast, low cost, and environmentally safe fabrication of materials. The observed phenomenology of the flash process encompasses multiple time and length scales and have resulted on a wide variety of what sometimes appears to be contradictory explanations. Here, this article summarizes the latest developments on the modeling and simulation of flash sintering, specifically those related to the understanding of the equilibrium and kinetic properties and the corresponding microstructural evolution of ionic ceramics. Challenges and opportunities in the development of theoretical analyses that include unidentified multiphysical effects are discussed, as they pertain to the processing of technologically relevant ceramic materials for advanced structures and devices.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Site Mixing for Engineering Magnetic Topological Insulators

The van der Waals compound, MnBi 2 Te 4 , is the first intrinsic magnetic topological insulator, providing a materials platform for exploring exotic quantum phenomena such as the axion insulator state and the quantum anomalous Hall effect. However, intrinsic structural imperfections lead to bulk conductivity, and the roles of magnetic defects are still unknown. With higher concentrations of the same types of magnetic defects, the isostructural compound MnSb 2 Te 4 is a better model system for a systematic investigation of the connections among magnetism, topology, and lattice defects. In this work, the impact of antisite defects on the magnetism and electronic structure is studied in MnSb 2 Te 4 . Mn-Sb site mixing leads to complex magnetic structures and tunes the interlayer magnetic coupling between antiferromagnetic and ferromagnetic. The detailed nonstoichiometry and site mixing of MnSb2Te4 crystals depend on the growth parameters, which can lead to ≈40% of Mn sites occupied by Sb and ≈15% of Sb sites by Mn in as-grown crystals. Single-crystal neutron diffraction and electron microscopy studies show nearly random distribution of the antisite defects. Band structure calculations suggest that the Mn-Sb site mixing favors a ferromagnetic interlayer coupling, consistent with experimental observation, but is detrimental to the band inversion required for a nontrivial topology. Overall, our results suggest a long-range magnetic order of Mn ions sitting on Bi sites in MnBi 2 Te 4 . The effects of site mixing should be considered in all layered heterostructures that consist of alternating magnetic and topological layers, including the entire family of MnTe(Bi 2 Te 3 )n, its Sb analogs, and their solid solution.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2025

Molecular dynamics study of grain boundaries as defect sinks under irradiation in LiAlO 2 and LiAl 5 O 8

Lithium aluminate ceramics, LiAlO 2 and LiAl 5 O 8 , show promise in nuclear environments due to their excellent radiation tolerance. Molecular dynamics simulations investigate grain boundaries (GB) and their role in defect evolution. Results reveal that GBs act as efficient defect sinks, with Li and Al atoms exhibiting distinct behaviors during displacement cascades. Tritium migration in LiAlO 2 is also studied, showing rapid diffusion and stable configurations with oxygen, corroborated by ab initio simulations from the literature. The calculated tritium diffusion coefficient of 1.33 × 10 - ¹⁴ m²/s aligns with the literature, validating the model. LiAl 5 O 8 demonstrates superior defect healing compared to LiAlO 2 , attributed to enhanced atomic transfer between grains and GBs. These findings reveal key insights into defect dynamics, providing essential insights for their application in tritium-producing burnable absorber rods (TPBARs).

36 MATERIALS SCIENCE↗

DOE OSTI2024

Decoding the Broadband Emission of 2D Pb-Sn Halide Perovskites through High-Throughput Exploration

Unlike single-component 2D metal halide perovskites (MHPs) exhibiting sharp excitonic photoluminescence (PL), a broadband PL emerges in mixed Pb-Sn 2D lattices. Two physical models –self-trapped exciton and defect-induced Stokes-shift – are proposed to explain this unconventional phenomenon. However, the explanations provide limited rationalizations without consideration of the formidable compositional space, and thus, the fundamental origin of broadband PL remains elusive. Herein, the high-throughput automated experimental workflow is established to systematically explore the broadband PL in mixed Pb-Sn 2D MHPs, employing PEA (Phenethylammonium) as a model cation known to work as a rigid organic spacer. Spectrally, the broadband PL becomes further broadened with rapid PEA 2 PbI 4 phase segregation with increasing Pb concentrations during early-stage crystallization. Counterintuitively, MHPs with high Pb concentrations exhibit prolonged PL lifetimes. Hyperspectral microscopy identifies substantial PEA 2 PbI 4 phase segregation in those films, hypothesizing that the establishment of charge transfer excitons by the phase segregation upon crystallization at high-Pb compositions results in distinctive PL properties. In conclusion, these results indicate that two independent mechanisms—defect-induced Stoke-shifts and the establishment of charge transfer excitons by phase segregation—coexist which significantly correlates with the Pb:Sn ratio, thereby simultaneously contributing to the broadband PL emission in 2D mixed Pb-Sn HPs.

2D Pb-Sn halide perovskites↗

DOE OSTI2025

Fusion of Experiments and Simulations for Real-Time Identification of Pipeline Defects

In this study, we explored fusion of experiments and simulations for real time identification of pipeline defects across physical and non-physical domains. The challenges associated to data processing were addressed and a combined classification models was presented via CNN models. In addition, regression model based on XGBOOST is built to determine the defect location and defect dimension from data-driven features of guided wave signals captured by SMS fiber optic sensor.

deep learning↗

DOE OSTI2025

Fusion of Experiments and Simulations for Real-Time Identification of Pipeline Defects

deep learning↗

DOE OSTI2021

Modeling the performance and faradaic efficiency of solid oxide electrolysis cells using doped barium zirconate perovskite electrolytes

Y-doped BaZrO 3 (BaZr 1 x Y x O 3 δ , or “BZY”), a proton-conducting ceramic featuring high bulk conductivity and good chemical stability, is a promising electrolyte material for solid oxide electrolysis cells. Further doping with Ce and/or Yb (creating materials “BCZY” and “BCZYYb”) can improve conductivity and sintering properties, but at significant penalty to cells’ faradaic efficiency (FE). Studies have proposed that reduction of lattice Ce can occur in the hydrogen electrode, which consumes some hydrogen produced by the hydrogen evolution reaction, leading to decreased FE. Despite studies suggesting this phenomenon, the mechanism is largely unknown. In this work, we developed a multiphysics model to study the transport of multiple defect species and the performance of BZY, BCZY, and BCZYYb, capturing the tradeoff between enhanced performance at the cost of FE for BCZY and BCZYYb electrolytes compared to BZY. We also found that increasing the water content of the anode gas supply lowers the current output of the cell but results in better FE. The model, which uses several parameters previously unavailable in the literature, was validated to experiments varying temperature, steam water content, and electrolyte material, as well as two performance metrics (performance curves and FE). Results verify and explain observed trends, informing future work on Ce-doped BZY electrolytes.

08 HYDROGEN↗

DOE OSTI2022

Visualizing the interplay of Dirac mass gap and magnetism at nanoscale in intrinsic magnetic topological insulators

In intrinsic magnetic topological insulators, Dirac surface-state gaps are prerequisites for quantum anomalous Hall and axion insulating states. Unambiguous experimental identification of these gaps has proved to be a challenge, however. Here, we use molecular beam epitaxy to grow intrinsic MnBi 2 Te 4 thin films. Using scanning tunneling microscopy/spectroscopy, we directly visualize the Dirac mass gap and its disappearance below and above the magnetic order temperature. We further reveal the interplay of Dirac mass gaps and local magnetic defects. We find that, in high defect regions, the Dirac mass gap collapses. Ab initio and coupled Dirac cone model calculations provide insight into the microscopic origin of the correlation between defect density and spatial gap variations. Finally, this work provides unambiguous identification of the Dirac mass gap in MnBi 2 Te 4 and, by revealing the microscopic origin of its gap variation, establishes a material design principle for realizing exotic states in intrinsic magnetic topological insulators.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2026

Optical properties of a diamond NV color center from capped embedded multiconfigurational correlated wavefunction theory

Diamond defects are among the most promising qubits. Modeling their properties through accurate quantum mechanical simulations can further their development into robust units of information. We use the recently developed capped density functional embedding theory (capped-DFET) with the multiconfigurational n-electron valence second-order perturbation theory to characterize the electronic excitation energies for different spin manifolds of the well-characterized negatively charged substitutional N defect adjacent to a vacancy (V C ) in diamond (N C V C − ). We successfully reproduce vertical excitation energies for both triplet and singlet states of N C V C − with errors < 0.1 eV. Unlike other embedding methods, capped-DFET exhibits robust predictions that are approximately independent of the embedded cluster size: it only requires a cluster to contain the defect atoms and their nearest neighbors (as small as a 40-atom capped cluster). Furthermore, our method is free from slowly converging Coulomb interactions between charged defects, and thus also only weakly dependent on supercell size.

Chemistry↗

DOE OSTI2025

Ab-initio informed cluster dynamics simulation of self- and Xe diffusivity in uranium mononitride under irradiation

Uranium mononitride (UN) is one of the ceramic nuclear fuel alternatives to oxide fuels considered for light water reactor and advanced reactor designs, as it presents significant advantages such as high uranium density (better economics) and high thermal conductivity and melting point (increased safety). Self- and fission gas diffusivities need to be better understood, given that they influence key fuel performance phenomena like swelling and fission gas release. Recently, radiation enhanced diffusivity was investigated in UN by means of cluster dynamics simulations relying on empirical potential-based parameterizations, the reliability of which highly depends on the interatomic potential accuracy. Here, in this work, we refine this approach by determining, using ab-initio calculations, the properties of defect clusters containing vacancies, self-interstitials and Xe impurities. We also consider larger clusters than previous studies. The obtained dataset (formation enthalpies, entropies, and migration barriers) is used to parameterize a cluster dynamics model of mobile clusters, and to calculate the defect cluster concentrations under irradiation. This gives us access to the radiation enhanced self- and fission gas diffusivities. Although the resulting diffusivities are close to the values reported in the literature, we find important qualitative differences in the diffusion mechanisms. Capturing the correct mechanisms is crucial to properly describe the chemistry and fission rate dependence of the model.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2022

Application of Orthogonal Defect Classification for Software Reliability Analysis

The modernization of existing and new nuclear power plants with digital instrumentation and control systems (DI&C) is a recent and highly trending topic. However, there lacks strong consensus on best-estimate reliability methodologies by both the United States (U.S.) Nuclear Regulatory Commission (NRC) and the industry. This has resulted in hesitation for further modernization projects until a more unified methodology is realized. In this work, we develop an approach called Orthogonal-defect Classification for Assessing Software Reliability (ORCAS) to quantify probabilities of various software failure modes in a DI&C system. The method utilizes accepted industry methodologies for software quality assurance that are also verified by experimental or mathematical formulations. In essence, the approach combines a semantic failure classification model with a reliability growth model to predict (and quantify) the potential failure modes of a DI&C software system. The semantic classification model is used to address the question: How do latent defects in software contribute to different software failure root causes? The use of reliability growth models is then used to address the question: Given the connection between latent defects and software failure root causes, how can we quantify the reliability of the software? A case study was conducted on a representative I&C platform (ChibiOS) running a smart sensor acquisition software developed by Virginia Commonwealth University (VCU). The testing and evidence collection guidance in ORCAS was applied, and defects were uncovered in the software. Qualitative evidence, such as condition coverage, was used to gauge the completeness and trustworthiness of the assessment while quantitative evidence was used to determine the software failure probabilities. The reliability of the software was then estimated and compared to existing operational data of the sensor device. It is demonstrated that by using ORCAS, a semantic reasoning framework can be developed to justify if the software is reliable (or unreliable) while still leveraging the strength of the existing methods.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2022

Application of Orthogonal Defect Classification for Software Reliability Analysis

The modernization of existing and new nuclear power plants with digital instrumentation and control systems (DI&C) is a recent and highly trending topic. However, there lacks strong consensus on best-estimate reliability methodologies by both the United States (U.S.) Nuclear Regulatory Commission (NRC) and the industry. This has resulted in hesitation for further modernization projects until a more unified methodology is realized. In this work, we develop an approach called Orthogonal-defect Classification for Assessing Software Reliability (ORCAS) to quantify probabilities of various software failure modes in a DI&C system. The method utilizes accepted industry methodologies for software quality assurance that are also verified by experimental or mathematical formulations. In essence, the approach combines a semantic failure classification model with a reliability growth model to predict (and quantify) the potential failure modes of a DI&C software system. The semantic classification model is used to address the question: How do latent defects in software contribute to different software failure root causes? The use of reliability growth models is then used to address the question: Given the connection between latent defects and software failure root causes, how can we quantify the reliability of the software? A case study was conducted on a representative I&C platform (ChibiOS) running a smart sensor acquisition software developed by Virginia Commonwealth University (VCU). The testing and evidence collection guidance in ORCAS was applied, and defects were uncovered in the software. Qualitative evidence, such as condition coverage, was used to gauge the completeness and trustworthiness of the assessment while quantitative evidence was used to determine the software failure probabilities. The reliability of the software was then estimated and compared to existing operational data of the sensor device. It is demonstrated that by using ORCAS, a semantic reasoning framework can be developed to justify software reliability (or unreliability) while still leveraging the strength of the existing methods.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2023

Can a deep-learning model make fast predictions of vacancy formation in diverse materials?

The presence of point defects, such as vacancies, plays an important role in materials design. Here, we explore the extrapolative power of a graph neural network (GNN) to predict vacancy formation energies. We show that a model trained only on perfect materials can also be used to predict vacancy formation energies (E vac ) of defect structures without the need for additional training data. Such GNN-based predictions are considerably faster than density functional theory (DFT) calculations and show potential as a quick pre-screening tool for defect systems. To test this strategy, we developed a DFT dataset of 530 E vac consisting of 3D elemental solids, alloys, oxides, semiconductors, and 2D monolayer materials. We analyzed and discussed the applicability of such direct and fast predictions. We applied the model to predict 192 494 E vac for 55 723 materials in the JARVIS-DFT database. Our work demonstrates how a GNN-model performs on unseen data.

2D materials↗

DOE OSTI2020

Indirectly probing the structural change in ion-irradiated Zr-Based metallic glasses from small scale mechanical tests

Ion irradiation was applied to tailor the structural heterogeneities in Zr-based metallic glasses at room temperature. Experimental methods of X-ray diffraction, nanoindentation, and micropillar compression were conducted to examine the irradiation effects on their structural and mechanical property changes. It is found that the irradiated materials retained amorphous structure after room-temperature Ni ion irradiation. The reduction of elastic modulus and hardness measured by nanoindentation indicated the irradiation-induced mechanical degradation. A unified statistic model was employed to quantitatively predict the density and strength of irradiation defects, although their specific structural and physical nature is not explicitly included in this model. The transition of non-intersecting shear bands to multiple intersecting shear bands was observed on compression tests of irradiated micropillars with the increase of irradiation dose. Furthermore, the analysis of the displacement excursion of micropillar compression tests indicated a different deformation mode from the unirradiated state. These results from nanoindentation pop-in and micro-pillar compression tests suggested that irradiation eventually leads to a new state with different types and characteristics of structural heterogeneities from violent displacement cascades and non-equilibrium energy deposition/dissipation processes, which also proves ion irradiation as an effective method to tune the structure and mechanical properties of metallic glasses.

36 MATERIALS SCIENCE↗