Regression-based projection for learning Mori-Zwanzig operators
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Results for “Computational materials science”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Experimental X-ray and fast neutron CT comparative analysis
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Engineering - Instrumentation related to nuclear s↗
Accessible Open-Source Software Framework for Multi-Scale Modeling
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36 MATERIALS SCIENCE↗
High-throughput phase-field simulations and machine learning of resistive switching in resistive random-access memory
Metal oxide-based Resistive Random-Access Memory (RRAM) exhibits multiple resistance states, arising from the activation/deactivation of a conductive filament (CF) inside a switching layer. Understanding CF formation kinetics is critical to achieving optimal functionality of RRAM. Here a phase-field model is developed, based on materials properties determined by ab initio calculations, to investigate the role of electrical bias, heat transport and defect-induced Vegard strain in the resistive switching behavior, using MO 2- x systems such as HfO 2- x as a prototypical model system. It successfully captures the CF formation and resultant bipolar resistive switching characteristics. High-throughput simulations are performed for RRAMs with different material parameters to establish a dataset, based on which a compressed-sensing machine learning is conducted to derive interpretable analytical models for device performance (current on/off ratio and switching time) metrics in terms of key material parameters (electrical and thermal conductivities, Vegard strain coefficients). These analytical models reveal that optimal performance (i.e., high current on/off ratio and low switching time) can be achieved in materials with a low Lorenz number, a fundamental material constant. This work provides a fundamental understanding to the resistive switching in RRAM and demonstrates a computational data-driven methodology of materials selection for improved RRAM performance, which can also be applied to other electro-thermo-mechanical systems.
36 MATERIALS SCIENCE↗
Computed Tomography Support for Microgravity Materials Science Experiments
The accurate measurement of density in both liquid and solid samples is of considerable interest to Principal Investigators with materials science experiments slated for the ISS. The work to be described is an innovative application of a conventional industrial nondestructive evaluation instrument. Traditional applications of industrial computed tomography (CT) rely on reconstructing cross sections of large structures to provide two-dimensional planar views which can identify defects such as porosity, or other material anomalies. This has been done on microgravity materials science experiments to check the integrity of ampoule-cartridge assemblies for safety purposes. With a substantially monoenergetic flux, as can be obtained with a radioactive cobalt source, there will be a direct correlation between absorption and density. Under such conditions it then becomes possible to make accurate measurements of density throughout a sample, and even when the sample itself is enclosed within a furnace and a safety required cartridge. Such a system has been installed at Kennedy Space Center (KSC) and is available to PIs to examine samples before and after flight. The CT system is being used to provide density information for two purposes. Firstly, the determination of density changes from liquid to solid is vital information to the PI for purposes of modeling the solidification behavior of his sample, and to engineers who have to design containment ampoules and must allow for shrinkage and other volume changes that may occur during processing. While such information can be obtained by pycnometric measurements, the possibility of using a furnace installed on the CT system enables one to examine potentially dangerous materials having high vapor pressures, while not needing visible access to the material. In addition, uniform temperature can readily be obtained, and the system can be controlled to ramp up, hold, and ramp down while collecting data over a wide range of parameters automatically. Results of initial tests on low melting point elements such as gallium, indium and tin will be presented, and the intent is to proceed to compounds such as InSb, HgCdTe and CdTe. Alloys such as Pb-Sb (PI - Poirier, U AZ) and Cu-Al (PI - Trivedi, Ames Lab.), which are the subjects of flight experiments, will also be examined. The second application is the conversion of measured density values directly to composition. This was successfully done with the mercury cadmium telluride alloys grown on the second and fourth United States Microgravity Payload (USMP-2 and USMP-4) missions by Lehoczky. CdTe values along the length of the boules were obtained at KSC prior to cutting the sample, and could have been obtained prior to its removal from the cartridge and ampoule. Examples of the data obtained will be shown. It is anticipated that several of the materials science PIs will avail themselves of the technique described, initially for determining densities prior to flight, and then to acquire early quantitative data on the compositional variation within their samples.
Gillies, Donald C.↗
Thermal gradient effect on helium and self-interstitial transport in tungsten
First-wall materials in a fusion reactor are expected to withstand harsh conditions, with high heat and particle fluxes that modify the materials microstructure. These fluxes will create strong gradients of temperature and concentration of diverse species. Besides the He ash and the hydrogenic species, neutron particles generated in the fusion reaction will collide with the material creating intrinsic defects, such as vacancies, self-interstitials atoms (SIAs), and clusters of such point defects. These defects and the He atoms will then migrate in the presence of the aforementioned gradients. In this study, we use nonequilibrium molecular dynamics to analyze the transport of He and SIAs in the presence of a thermal gradient in tungsten. We observe that, in all cases, the defects and impurity atoms tend to migrate toward the hot regions of the tungsten sample. The resulting species concentration profiles are exponential distributions, rising toward the hot regions of the sample, in agreement with irreversible thermodynamics analysis. For both He atoms and SIAs, we find that the resulting species flux is directed opposite to the heat flux, indicating that species transport is governed by a Soret effect (thermal-gradient-driven diffusion) characterized by a negative heat of transport that drives species diffusion uphill (from the cooler to the hot regions of the sample). Here, we demonstrate that the steady-state species profiles obtained accounting for the Soret effect vary significantly from those where temperature-gradient-driven transport is not considered and discuss the implications of such a Soret effect on the response to plasma exposure of plasma-facing tungsten.
36 MATERIALS SCIENCE↗
FY24 LLNL Laboratory Directed Research and Development (LDRD) Project Accomplishments
Large duration energy storage is the key to couple renewable energy generation with power supply. This project aimed to advance the durability of a low-cost and eco-friendly flow battery technology based on iron chemistry to speed up the technology readiness level rapidly and radically. A novel device which is called “an artificial kidney” was innovated through disruptive research to integrate with the flow battery for rebalancing capacity. Key results at 50 cm2 scale demonstrates that artificial kidney enabled retaining the storage capacity of the flow battery over 100 cycles. Comparison of outcome of this work showing 0% capacity degradation to the state-of-the-art technology corroborates that this novel system is a game changing innovation.
99 GENERAL AND MISCELLANEOUS↗
Autonomous reinforcement learning agent for chemical vapor deposition synthesis of quantum materials
Abstract Predictive materials synthesis is the primary bottleneck in realizing functional and quantum materials. Strategies for synthesis of promising materials are currently identified by time-consuming trial and error and there are no known predictive schemes to design synthesis parameters for materials. We use offline reinforcement learning (RL) to predict optimal synthesis schedules, i.e., a time-sequence of reaction conditions like temperatures and concentrations, for the synthesis of semiconducting monolayer MoS 2 using chemical vapor deposition. The RL agent, trained on 10,000 computational synthesis simulations, learned threshold temperatures and chemical potentials for onset of chemical reactions and predicted previously unknown synthesis schedules that produce well-sulfidized crystalline, phase-pure MoS 2 . The model can be extended to multi-task objectives such as predicting profiles for synthesis of complex structures including multi-phase heterostructures and can predict long-time behavior of reacting systems, far beyond the domain of molecular dynamics simulations, making these predictions directly relevant to experimental synthesis.
36 MATERIALS SCIENCE↗
Grayscale Digital Light Processing Gradient Printing for Stress Concentration Reduction and Material Toughness Enhancement
Avoiding stress concentrations is essential to achieve robust parts since failure tends to originate at such concentrations. With recent advances in multimaterial additive manufacturing, it is possible to alter the stress (or strain) distribution by adjusting the material properties in selected locations. Here, we investigate the use of grayscale digital light processing (g-DLP) 3D printing to create modulus gradients around areas of high stress. These gradients prevent failure by redistributing high stresses (or strains) to the neighboring material. The improved material distributions are calculated using finite element analysis. The much-enhanced properties are demonstrated experimentally for thin plates with circular, triangular, and elliptical holes. In conclusion, this work suggests that multimaterial additive manufacturing techniques like g-DLP printing provide a unique opportunity to create tougher engineering materials and parts.
36 MATERIALS SCIENCE↗
International Workshop on Recent Developments in Electronic Structure (Final Report)
This is the final report of the 2022 International Workshop on Recent Developments in Electronic Structure (ES22) that took place in New York City between May 31 and June 3, 2022.
36 MATERIALS SCIENCE↗
Revisiting Néel 60 years on: The magnetic anisotropy of L1 0 FeNi (tetrataenite)
The magnetocrystalline anisotropy energy of atomically ordered L1 0 FeNi (the meteoritic mineral tetrataenite) is studied within a first-principles electronic structure framework. Two compositions are examined: equiatomic Fe 0.5 Ni 0.5 and an Fe-rich composition, Fe 0.56 Ni 0.44 . It is confirmed that, for the single crystals modeled in this work, the leading-order anisotropy coefficient K 1 dominates the higher-order coefficients K 2 and K 3 . To enable comparison with experiment, the effects of both imperfect atomic long-range order and finite temperature are included. While our computational results initially appear to undershoot the measured experimental values for this system, careful scrutiny of the original analysis due to Néel et al. [J. Appl. Phys. 35, 873 (1964)] suggests that our computed value of K 1 is, in fact, consistent with experimental values, and that the noted discrepancy has its origins in the nanoscale polycrystalline, multivariant nature of experimental samples, that yields much larger values of K 2 and K 3 than expected a priori. These results provide fresh insight into the existing discrepancies in the literature regarding the value of tetrataenite’s uniaxial magnetocrystalline anisotropy in both natural and synthetic samples.
36 MATERIALS SCIENCE↗
A novel design of transitional layer structure between reduced activation ferritic martensitic steels and tungsten for plasma facing materials
Plasma-facing components (PFCs) are among the most critical gaps for fusion energy to establish technical and economic feasibility. Tungsten as a first wall/blanket material in PFCs requires to be integrating with reduced activation ferritic martensitic (RAFM) steels as a structural component. Currently, major drawbacks are the requirement of brazing, the formation of a brittle interface, and a large difference between the coefficients of thermal expansion of tungsten and steel. Here, a novel transitional multilayer structure was designed and investigated to join tungsten and RAFM steels using three interlayers. The composition of each interlayer was selected based on computational thermodynamics and diffusion kinetics to ensure a body-centered cubic (bcc) single-phase structure and prevent the formation of a brittle intermetallic phase region in the temperature range of 600–1150 °C. Although the transitional layer structure was designed for additive manufacturing, spark plasma sintering (SPS) as proof of concept was used to bond the individual layers. Interfaces were investigated using scanning and transmission electron microscopy methods but no layered intermetallic phase was observed. Nanoindentation maps across the interface suggest major hardness differences at the interface between tungsten and the vanadium interlayer, as well as the interface between RAFM steel and the FeCrAl interlayer.
36 MATERIALS SCIENCE↗
3D micromechanical simulation of PBX composites
Previous research studied the constitutive response and interface strength of a bonded bicrystal system containing two HMX explosive grains bound with a HTPB polymer binder by performing FE simulations on real geometry imaged with micro Computed Tomography (µCT). The parameters generated from this past study were successfully applied to 2D mesoscale simulations of µCT imaged HMX-HTPB polycrystal samples. Presently, the mechanical response of PBXs containing a Nitro-Plasticized Estane (NPE) binder with similar microstructural geometry to the HMX-HTPB polycrystal samples were studied. Imaging of a HMX-NPE bicrystal sample allowed for simulation of the new material pairing. However, imaging using µCT techniques on the new polycrystal samples was difficult due to the decrease in contrast between the HMX grains and the NPE binder. Mesoscale polycrystal simulations utilizing a single µCT imaged microstructure were performed on a 3D representative volume element (RVE) comparing the delamination behavior and mechanical response of both HMX-HTPB and HMX-NPE PBXs. Furthermore, this technique shows the ability to virtually explore the mechanical response of a range of hypothetical materials that share common microstructural geometric characteristics in addition to studying numerically altered geometries.
36 MATERIALS SCIENCE↗
Numerical modeling of fiber orientation in multi-layer, isothermal material-extrusion big area additive manufacturing
Not Available
36 MATERIALS SCIENCE↗
Science and Technology Review June 2021
When the COVID-19 pandemic hit, Lawrence Livermore National Laboratory scientists came together, leveraging many different disciplines and technologies to address this global challenge. Livermore focused on three areas of research: creating alternatives for medical equipment in short supply, such as ventilators and nasal swabs; developing methods to help detect the SARS-CoV-2 virus responsible for COVID-19; and designing medical countermeasures such as antibodies and antivirals to combat the disease.
36 MATERIALS SCIENCE↗
The role of pre-existing heterogeneities in materials under shock and spall
There has been a challenge for many decades to understand how heterogeneities influence the behavior of materials under shock loading, eventually leading to spall formation and failure. Experimental, analytical, and computational techniques have matured to the point where systematic studies of materials with complex microstructures under shock loading and the associated failure mechanisms are feasible. This is enabled by more accurate diagnostics as well as characterization methods. As interest in complex materials grows, understanding and predicting the role of heterogeneities in determining the dynamic behavior becomes crucial. Early computational studies, hydrocodes, in particular, historically preclude any irregularities in the form of defects and impurities in the material microstructure for the sake of simplification and to retain the hydrodynamic conservation equations. Contemporary computational methods, notably molecular dynamics simulations, can overcome this limitation by incorporating inhomogeneities albeit at a much lower length and time scale. This review discusses literature that has focused on investigating the role of various imperfections in the shock and spall behavior, emphasizing mainly heterogeneities such as second-phase particles, inclusions, and voids under both shock compression and release. Pre-existing defects are found in most engineering materials, ranging from thermodynamically necessary vacancies, to interstitial and dislocation, to microstructural features such as inclusions, second phase particles, voids, grain boundaries, and triple junctions. This literature review explores the interaction of these heterogeneities under shock loading during compression and release. Systematic characterization of material heterogeneities before and after shock loading, along with direct measurements of Hugoniot elastic limit and spall strength, allows for more generalized theories to be formulated. Further, continuous improvement toward time-resolved, in situ experimental data strengthens the ability to elucidate upon results gathered from simulations and analytical models, thus improving the overall ability to understand and predict how materials behave under dynamic loading.
36 MATERIALS SCIENCE↗
Tardigrade (Quarterly Report Q1 FY2022)
Tardigrade is an effort to create tunable thermal expansion materials for use in a ruggedized lens housing. This requires new computational algorithms to model and predict thermal performance of meta-materials composed of multiple structured base materials and void-space, along with matching and developing 3D print technology to demonstrate the housing. The project is 27 months into the 36-month plan. We are continuing effort on 3 main technical thrusts: material design algorithm development (transitioning into full 3D design), multi-material additive manufacturing process exploration, and sensor/optics design and engineering. In the last quarter, we have focused on developing the code for 3D Topology Optimization, building data sets for 2D autoencoder training, and finishing lens housing engineering for the microbolometer based test sensor. We are exploring 2 potential multi-material AM technologies, a polymer-based technique in development at LLNL and a metal SLS system produced by Aconity, called the AeroSint deposition head.
36 MATERIALS SCIENCE↗
Design guidance for ferrites: Insights from density functional theory on magnetic properties
A grand challenge in materials research is identifying the relationship between composition and performance. Herein, we explore this relationship for magnetic properties, specifically magnetic saturation (M s ) and magnetocrystalline anisotropy energy (K) of ferrites. Ferrites are materials derived from magnetite (chemical formula = Fe 3 O 4 ) that comprise metallic elements such as Fe, Mn, Ni, Co, Cu and Zn. Experimentally, synthesizing and characterizing ferrites is time consuming. Further, selection of compositions to achieve particular magnetic properties currently relies on intuition. To address this, in this work, density functional theory (DFT) is used to predict M s and K for 571 ferrite structures. These structures are primarily double-substituted non-stoichiometric ferrites with formulae M1 x M2 y Fe 3–x–y O 4 , where M1 and M2 can be Mn, Ni, Co, Cu and/or Zn and 0 ≤ x ≤ 1 and y = 1–x. Calculated magnetic properties for the structures in this study vary from 0.04 × 10 5 to 9.6 × 10 5 A m −1 for M s and from 0.02 × 10 5 to 14.08 × 10 5 J m −3 for K. All structures are made publicly available in a FAIR database.
36 MATERIALS SCIENCE↗