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DOE OSTI2025

Particle dynamics in the spatial afterglow of flowing dusty plasmas

Final technical report for project Department of Energy - DE-SC0022242. The overarching hypothesis of this project was that the particle dynamics in the afterglow of dusty plasmas are determined by the interplay of the transient plasma properties and the material properties of dust particles. By combining experiments and modeling in an approach fundamentally different from prior research, this project investigated correlations between material properties and basic dust charging and decharging mechanisms.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2023

Post Irradiation Examination Results of Irradiated Yttrium Hydride

Department of Energy’s (DOE’s) Microreactor program (MRP) aims to provide the fundamental data to enable the development of microreactors. As such, material property data of critical materials for microreactor technologies are researched. Because substoichiometric yttrium dihydride (YHx, where x<2) is considered as a potential solid neutron moderator, its material property data has been combined in the Advanced Moderator Material Handbook which includes thermodynamic and thermophysical properties of YHx with the exception of irradiated material’s properties due to limited PIE. To fill the knowledge gap for the irradiated YHx, specimens and irradiation capsules were prepared at Los Alamos National Laboratory (LANL). Specimens were irradiated in the Advanced Test Reactor (ATR) at Idaho National Laboratory’s (INL’s). post-irradiation examination (PIE) was performed at INL’s Materials and Fuels Complex (MFC). This report compiles the PIE results of irradiated YHx specimens through fiscal years 2022 and 2023 (FY22-23) . The PIE data will directly be incorporated into the newer version of the Advanced Moderator Material Handbook. The main takeaways include that (i) the geometrical stability and mechanical integrity of YHx was intact with couple exceptions after high-temperature irradiations (600-800°C), (ii) hydrogen content variation due to manufacturing or irradiation in YHx caused visible surface discoloration, that is also related to the microstructural changes, (iii) qualitative comparisons of PIE methods implied that H retention was significantly higher at 600°C as compared to 800°C, (iv) thermal properties included signatures correlated with the H loss or re-gain, (v) importance of manufacturing readiness and initial as-manufactured specimens history was emphasized, (vi) the needs of targeted irradiations focusing on temperature and time parameters and very targeted PIE were specified.

08 HYDROGEN↗

DOE OSTI2023

Report on the Incorporation of Post-Irradiation Examination Results of Yttrium Hydride for Advanced Moderator Handbook

08 HYDROGEN↗

DOE OSTI2022

Multi-task graph neural networks for simultaneous prediction of global and atomic properties in ferromagnetic systems *

Abstract We introduce a multi-tasking graph convolutional neural network, HydraGNN, to simultaneously predict both global and atomic physical properties and demonstrate with ferromagnetic materials. We train HydraGNN on an open-source ab initio density functional theory (DFT) dataset for iron-platinum with a fixed body centered tetragonal lattice structure and fixed volume to simultaneously predict the mixing enthalpy (a global feature of the system), the atomic charge transfer, and the atomic magnetic moment across configurations that span the entire compositional range. By taking advantage of underlying physical correlations between material properties, multi-task learning (MTL) with HydraGNN provides effective training even with modest amounts of data. Moreover, this is achieved with just one architecture instead of three, as required by single-task learning (STL). The first convolutional layers of the HydraGNN architecture are shared by all learning tasks and extract features common to all material properties. The following layers discriminate the features of the different properties, the results of which are fed to the separate heads of the final layer to produce predictions. Numerical results show that HydraGNN effectively captures the relation between the configurational entropy and the material properties over the entire compositional range. Overall, the accuracy of simultaneous MTL predictions is comparable to the accuracy of the STL predictions. In addition, the computational cost of training HydraGNN for MTL is much lower than the original DFT calculations and also lower than training separate STL models for each property.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2023

Exploring finite temperature properties of materials with quantum computers

Abstract Thermal properties of nanomaterials are crucial to not only improving our fundamental understanding of condensed matter systems, but also to developing novel materials for applications spanning research and industry. Since quantum effects arise at the nano-scale, these systems are difficult to simulate on classical computers. Quantum computers can efficiently simulate quantum many-body systems, yet current quantum algorithms for calculating thermal properties of these systems incur significant computational costs in that they either prepare the full thermal state on the quantum computer, or they must sample a number of pure states from a distribution that grows with system size. Canonical thermal pure quantum (TPQ) states provide a promising path to estimating thermal properties of quantum materials as they neither require preparation of the full thermal state nor require a growing number of samples with system size. Here, we present an algorithm for preparing canonical TPQ states on quantum computers. We compare three different circuit implementations for the algorithm and demonstrate their capabilities in estimating thermal properties of quantum materials. Due to its increasing accuracy with system size and flexibility in implementation, we anticipate that this method will enable finite temperature explorations of relevant quantum materials on near-term quantum computers.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Active learning path-dependent properties using a cloud-based materials acceleration platform

Solid state materials are central to many modern technologies in which a given material may be exposed to a variety of environments. The material properties often vary with the sequence of environments in an irreversible manner, resulting in a quintessential path-dependency in experimental observables. While sequential learning techniques have been effectively deployed for accelerating learning of state properties of materials, they often use a consistent environment path in all experiments. To elevate such techniques for making optimal decisions in experimental investigations of path-dependent properties, we introduce an iterated expected information gain acquisition function that optimizes over entire experimental trajectories. This approach is implemented within a cloud-based Materials Acceleration Platform architecture utilizing an event-driven stateful broker coupled with remote HELAO (Hierarchical Experimental Laboratory Automation and Orchestration) instances and an AI science manager. The platform's efficacy was demonstrated through a case study optimizing multi-step spectro-electrochemical experiments to identify optically stable potential windows in (Co–Ni–Sb)O z metal oxides. The system successfully integrated AI-driven experiment design, remote laboratory automation, and cloud-based data infrastructure, validating the platform's capability for managing complex, adaptive, path-dependent workflows in materials discovery.

Guevarra, Dan [California Institute of Technology ↗

DOE OSTI2026

Connect microstructure evolution to property degradation with validated simulation

Reliable material performance is required for plasma-facing material (PFM) candidates. Previous research has shown that plasma and neutron radiation exposure induces microstructural changes in PFMs; changes in thermal and electrical conductivities and in material hardening and embrittlement were also observed after neutron irradiation. These material property changes will negatively impact the performance of the PFMs in a fusion reactor. Despite the well-known connection between material microstructure, properties, and performance, there is a need for validated modeling capabilities connecting PFM property degradation with microstructural evolution under fusion-relevant conditions. We are developing a simulation capability to couple plasma-induced microstructural evolution to material property degradation. Our approach relies on deliberate mapping between individual simulation models and experimental characterization for validation. The open-source Multiphysics Object-Oriented Simulation Environment (MOOSE) software was used for this simulation capability development. A MOOSE phase-field model was coupled with the cluster dynamics code, Xolotl, to predict microstructural evolution. Microstructure characterization techniques, including scanning electron microscopy (SEM), transmission electron microscopy (TEM), and laser scanning confocal microscopy (LSCM) are used to validate these microstructural evolution simulations. Calculation of thermal and electrical conductivities with first principles simulations was performed for bulk material and for grain boundaries; these results are used within MOOSE models to calculate effective thermal and electrical conductivities as a function of grain characteristics. Thermoreflectance and four-probe techniques were employed to measure the thermal and electrical conductivities, respectively. A MOOSE crystal plasticity model was adapted to predict microstructure-sensitive deformation behavior, and X-ray diffraction (XRD) was used to collect bulk dislocation density data for validation. After individual simulation validation, these models are coupled to predict material property changes resulting from plasma exposure. We focused here on an experimental design to emphasize the separate effects of moderate thermal loads and plasma exposure using tungsten. Annealing of tungsten was performed under a protective environment for temperatures ranging from 500$^o$C to 1500$^o$C. The plasma exposure was completed in the Tritium Plasma Experiment at Idaho National Laboratory under a deuterium flux of 1e22 $\frac{D}{m^2s}$. This incremental approach is employed to build confidence in the modeling capability: separate-effects tests ensure that the models capture key mechanisms from single environmental conditions before predicting PFM property degradation under combined loads. We will show our early results from coupling these simulation models to predict PFM property changes from microstructural evolution. Comparisons of the simulation results with preliminary validation data will be discussed.

36 - MATERIALS SCIENCE↗

DOE OSTI2024

ElasTool v3.0: Efficient computational and visualization toolkit for elastic and mechanical properties of materials

Efficient computation and visualization of elastic and mechanical properties are crucial in the selection of materials and the design of new materials. Here, the ElasTool v3.0 toolkit marks a significant advancement in the computational analysis and visualization of elastic and mechanical properties of materials, essential in material selection and design. This enhanced version extends beyond standard calculations like elastic tensor, Young's modulus, bulk modulus, and Poisson's ratio. It introduces capabilities for computing minimum thermal conductivity, linear compressibility, rendering the Christoffel equation, and elastic energy density. Notably, it integrates advanced visualization tools, including compatibility with Plotly and Elate web platforms for interactive web-based property exploration. A key feature of ElasTool v3.0 is the implementation of second-order elastic constants (SOECs) for tubular 2D-based nanostructures and nanotubes. Leveraging high-efficiency strain-matrix sets (OHESS), the toolkit now facilitates efficient computation of elastic constants and mechanical properties at both zero and finite temperatures for 1D, 2D, and 3D dimensions. ElasTool is openly accessible on GitHub: https://github.com/gmp007/elastool.

1D, 2D, 3D, and tubular 2D nanostructure and nanot↗

DOE OSTI2023

How machine learning can extend electroanalytical measurements beyond analytical interpretation

Electroanalytical measurements are routinely used to estimate material properties exhibiting current and voltage signatures. Analysis of such measurements relies on analytical expressions of material properties to describe the experiments. The need for analytical expressions limits the experiments that can be used to measure properties as well as the properties that can be estimated from a given experiment. Such analytical relations are essentially solutions of the physics-based differential equations (with properties as coefficients) describing the material behavior under certain specific conditions. In recent years, a new machine learning-based approach has been gaining popularity wherein the differential equations are numerically solved to interpret the electroanalytical experiments in terms of corresponding material properties. Since the physics-based differential equations are solved, one can additionally estimate underlying fields, e.g., concentration profile, using such an approach. To exemplify the characteristics of such a machine learning assisted interpretation of electroanalytical measurements, we use data from the Hebb–Wagner test on a magnesium spinel intercalation host. In conclusion, as compared to the traditional analytical expression-based interpretation, the emerging approach decreases experimental efforts to characterize relevant material properties as well as provides field information that was previously inaccessible.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Study of the Scintillating Properties of Materials Based on Self- Assembling Quantum Dots Embedded into a Semiconductor Bulk

The goal of the project is to investigate scintillating properties of materials based on self-assembled InAs QDs embedded into GaAs bulk. According to theoretical predictions, such materials can have scintillating properties and low self-absorption. Compared to scintillators based on inorganic scintillating crystals, QD-based scintillators could have significantly higher light yield and shorter emission times. We intend to check the theoretical predictions experimentally, measure scintillation parameters and their dependence on the QD-based materials and learn how the properties could be optimized for practical applications.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Effects of Impact and Target Parameters on the Results of a Kinetic Impactor: Predictions for the Double Asteroid Redirection Test (DART) Mission

Abstract The Double Asteroid Redirection Test (DART) spacecraft will impact into the asteroid Dimorphos on 2022 September 26 as a test of the kinetic impactor technique for planetary defense. The efficiency of the deflection following a kinetic impactor can be represented using the momentum enhancement factor, β , which is dependent on factors such as impact geometry and the specific target material properties. Currently, very little is known about Dimorphos and its material properties, which introduces uncertainty in the results of the deflection efficiency observables, including crater formation, ejecta distribution, and β . The DART Impact Modeling Working Group (IWG) is responsible for using impact simulations to better understand the results of the DART impact. Pre-impact simulation studies also provide considerable insight into how different properties and impact scenarios affect momentum enhancement following a kinetic impact. This insight provides a basis for predicting the effects of the DART impact and the first understanding of how to interpret results following the encounter. Following the DART impact, the knowledge gained from these studies will inform the initial simulations that will recreate the impact conditions, including providing estimates for potential material properties of Dimorphos and β resulting from DART’s impact. This paper summarizes, at a high level, what has been learned from the IWG simulations and experiments in preparation for the DART impact. While unknown, estimates for reasonable potential material properties of Dimorphos provide predictions for β of 1–5, depending on end-member cases in the strength regime.

58 GEOSCIENCES↗

DOE OSTI2024

Targeted materials discovery using Bayesian algorithm execution

Rapid discovery and synthesis of future materials requires intelligent data acquisition strategies to navigate large design spaces. A popular strategy is Bayesian optimization, which aims to find candidates that maximize material properties; however, materials design often requires finding specific subsets of the design space which meet more complex or specialized goals. We present a framework that captures experimental goals through straightforward user-defined filtering algorithms. These algorithms are automatically translated into one of three intelligent, parameter-free, sequential data collection strategies (SwitchBAX, InfoBAX, and MeanBAX), bypassing the time-consuming and difficult process of task-specific acquisition function design. Our framework is tailored for typical discrete search spaces involving multiple measured physical properties and short time-horizon decision making. We demonstrate this approach on datasets for TiO 2 nanoparticle synthesis and magnetic materials characterization, and show that our methods are significantly more efficient than state-of-the-art approaches. Overall, our framework provides a practical solution for navigating the complexities of materials design, and helps lay groundwork for the accelerated development of advanced materials.

42 ENGINEERING↗

DOE OSTI2023

Status on Development of Graphite Analytical Tool (GAT)

The DOE-ART Graphite R&D program has been generating significant amounts of irradiated and unirradiated graphite data since 2006 when the program was part of the DOE NGNP (Next Generation Nuclear Plant) Project. This data includes critical irradiation creep and irradiated material property changes from the Advanced Graphite Creep (AGR) experiment as well as significant amounts of data on unirradiated material property values on several current nuclear graphite grades (Baseline program). Previously, Idaho National Laboratory has developed an internal analysis tool to assist with analysis of the unirradiated and irradiated data. The Graphite Analytical Tool (GAT) is intended to provide easy access to the graphite data in the form of comparing unirradiated and irradiated material property changes, comparison of material property differences between various nuclear graphite grades, and illustrate trends within the irradiated and unirradiated data generated within the DOE-ART Graphite R&D program. This report summarizes the progress to-date on the development of this analytical tool.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2022

An Anomalous Electron Configuration Among 3d Transition Metal Atoms

Physical properties of materials are mainly determined by valence electron configurations, where different valence shells would induce divergent phenomena. In compounds containing Sc 2+ , 3d electron occupancy is expected, same as other transition metal atoms like Ti 3+ . But this situation still awaits experimental verification. In this work, we selected ScS to measure the valence electron density and orbital population of Sc2+ through delicate quantitative convergent-beam electron diffraction. Though the [Ar]3d 1 configuration is confirmed in ionic Sc 2+ , the unintuitive occupation of 4s orbital is concluded with the absence of 3d orbital features around Sc-atom sites and nearly bare population of t2g orbital. It should be the first time to report this special electron configuration in a transition metal compound, in which 4s rather than 3d orbital is preferred. Our findings reveal the distinct behavior of Sc, and probable ways to modulate material properties by controlling electron orbitals.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Structural Dynamics in UV Curable Resins Resolved by In Situ 3D Printing X-ray Photon Correlation Spectroscopy

Additive manufacturing (AM) is a promising technique to rapidly produce polymeric materials into complex 3-dimensional (3D) geometries. While AM is widespread and relevant for a range of applications, implementation in industry has outpaced our fundamental understanding of polymer dynamics and structure development during the printing process. Characterization and quantification of such dynamics is necessary to optimize final material properties and design future materials and processes for 3D printing. Here, we utilize X-ray photon correlation spectroscopy (XPCS) to measure spatial and time-resolved, out-of-equilibrium dynamics during direct ink write (DIW) 3D printing. Specifically, we investigate the progression of structural dynamics in a dual cure (UV/thermal) nanocomposite during and directly after printing. As the filament is printed and cured in situ, the relaxation processes of the cross-linking network are measured through the dynamics of inorganic filler particles. The characteristic relaxation time of the dynamics is calculated through the intensity–intensity autocorrelation function g 2 and directly correlated to the printing process parameters, such as printhead velocity and UV light intensity. The time-resolved evolution of nanoscale dynamics follows a power-law dependence as the filament is cured. Additionally, bulk rheological characterizations reveal the macroscopic solidification of the resin, providing correlation of material properties across a wide range of length and time scales. Overall, the measurement of multiscale, out-of-equilibrium dynamics provides insight into the development of structure in polymer nanocomposite filaments during 3D printing and is used to further understand the influence of such parameters on the AM process.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Survey of Modeling and Simulation Techniques for Advanced Manufacturing Technologies Volume I – Predicting Initial Microstructures

This report summarizes the current state of modeling and simulation methods for predicting the initial structure and properties of material in components assembled using advanced manufacturing technologies (AMTs). The report is the first volume in a two-volume series. This first volume focuses on predicting initial microstructures of AM material. The second volume discusses predicting material properties given the initial microstructure. The focus is on technologies of particular relevance to the design and manufacture of nuclear reactor components. The purpose of the report is to help develop the technical knowledge base to support regulatory decisions that will be needed to assess nuclear components manufactured with AMTs as these components are installed at nuclear power plants (NPPs). The report develops a list of AMTs of particular interest to the NRC and summarizes the key microstructural features and corresponding processing parameters relevant to each technology. Further sections describe available physically-based and data-driven prediction methods and survey widely available software tools. The report concludes with a summary of gaps that may be of particular interest to the NRC when evaluating modeling and simulation methods for AMTs.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

Area-based composition predictions of materials fabricated using simultaneous wire-powder-directed energy deposition

Functionally graded materials are an emergent method for designing components with programmable site-specific material properties. These materials are typically fabricated using metal additive manufacturing tools by simultaneously feeding multiple wire and/or powder feedstocks at various rates to achieve spatial composition change. The wire-powder-directed energy deposition (WP-DED) technique is of particular interest for many functionally graded material applications by balancing the low raw materials cost of wire with the high resolution of powder. However, feeding wire and powder are inherently different processes since all extruded wire enters the melt pool, while much of the blown powder is scattered, which makes determining the composition of the build challenging. In this study, we devise a simple area-based measurement method for estimating the composition of WP-DED structures. WP-DED single beads are printed using 309L stainless steel wire and commercially pure Fe powder at five wire feed rates (0.5, 0.75, 1.00, 1.25, 1.50 mm/mm) and five powder feed rates (2, 4, 6, 8, 10 rpm). Characteristic defects including interface gaps and macrosegregation (lack of mixing) tendencies are examined. High powder feed rates (8, 10 rpm) result in interface gaps at all wire feed rates, but smooth deposition and complete mixing is achieved at low powder feed rates, particularly with lower wire feed rates as well. The area-based composition measurement method is within ±20% of energy dispersive x-ray spectroscopy measurements for all samples, showing its effectiveness as a rapid composition estimate for WP-DED materials development.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Unconventional colloidal aggregation in chiral bacterial baths

Abstract When in equilibrium, thermal forces agitate molecules, which then diffuse, collide and bind to form materials. However, the space of accessible structures in which micron-scale particles can be organized by thermal forces is limited, owing to the slow dynamics and metastable states. Active agents in a passive fluid generate forces and flows, forming a bath with active fluctuations. Two unanswered questions are whether those active agents can drive the assembly of passive components into unconventional states and which material properties they will exhibit. Here we show that passive, sticky beads immersed in a bath of swimmingEscherichia colibacteria aggregate into unconventional clusters and gels that are controlled by the activity of the bath. We observe a slow but persistent rotation of the aggregates that originates in the chirality of theE. coliflagella and directs aggregation into structures that are not accessible thermally. We elucidate the aggregation mechanism with a numerical model of spinning, sticky beads and reproduce quantitatively the experimental results. We show that internal activity controls the phase diagram and the structure of the aggregates. Overall, our results highlight the promising role of active baths in designing the structural and mechanical properties of materials with unconventional phases.

Physics↗