LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales
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74 ATOMIC AND MOLECULAR PHYSICS↗
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Quantum Computing to Accelerate High Fidelity Computational Materials Modeling
In this CIF we worked to develop quantum algorithms for material science simulations based on new ideas recently proposed on plane wave basis sets. Many body simulations are not generally performed in plane wave basis sets on classical hardware, but with newly proposed quantum algorithms, it is possible this will be a highly efficient basis set to run quantum simulations on quantum hardware. Using state of the art classical simulations we ran small test simulations to estimate the resources that will be needed to run such plane wave algorithms on quantum hardware. We demonstrate our approach for a series of atoms and molecular systems.
Norman Tubman↗
Summary of Structural Material Modeling Development for the NEAMS Program in Fiscal Year 2020
This report summarizes work performed during Fiscal Year (FY) 2020 at Idaho National Laboratory (INL) for the U.S. Department of Energy?s Nuclear Engineering Advanced Modeling and Simulation (NEAMS) program for the Structural Materials and Chemistry Technical Area in the work package entitled "MS- 20IN050104 - Structural Materials - INL." The Structural Materials and Chemistry Technical Area is a relatively new component of the NEAMS program, and is currently focusing on developing simulation capabilities to support the deployment of nuclear energy in the areas of molten salt reactor chemistry, light water reactor (LWR) structural material degradation, and structural material behavior for advanced reactor applications. INL performed work for to advance capabilities for simulation of structural material behavior in both LWR and advanced reactor applications in the work described here.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
LL13-MatModelRadDetect-PD2Jf Final Report: Materials Modeling for High-Performance Radiation Detectors
The aims of this project are to enable rational materials design for select high-payoff challenges in radiation detection materials by using state-of-the-art predictive atomistic modeling techniques. Three specific high-impact challenges are addressed: (i) design and optimization of electrical contact stacks for TlBr detectors to stabilize temporal response at room-temperature; (ii) identification of chemical design principles of host glass materials for large-volume, low-cost, highperformance glass scintillators; and (iii) determination of the electrical impacts of dislocation networks in Cd 1-x Zn x Te (CZT) that limit its performance and usable single-crystal volume. The specific goals are to establish design and process strategies to achieve improved materials for high performance detectors. Each of the major tasks is discussed below in three sections, which include the goals for the task and a summary of the major results, followed by a listing of publications that contain the full details, including details of the methodologies used. The appendix lists 12 conference presentations given for this project, including 1 invited talk and 1 invited poster.
36 MATERIALS SCIENCE↗
Additive Manufacturing with Cellulose-Based Composites: Materials, Modeling, and Applications
Recent advances in large-scale additive manufacturing (AM) with polymer-based composites have enabled efficient production of high-performance materials. Cellulose nanomaterials (CNMs) have emerged as bio-based feedstocks due to their exceptional strength and sustainability. However, challenges such as hornification and poor dispersion in polymer matrices still limit large-scale CNM–polymer composite manufacturing, requiring novel strategies. Here, this review outlines an approach starting with atomic-level simulations to link molecular composition to key parameters like bulk density, viscosity, and modulus. These simulations provide data for finite element analysis (FEA), which informs large-scale experiments and reduces the need for extensive trials. The strategy explores how atomic interactions impact the morphology, adhesion, and mechanical properties of CNM-based composites in AM processes. The review also discusses current developments in AM, along with predictions of mechanical and thermal properties for structural applications, packaging, flexible electronics, and hydrogel scaffolds. By integrating experimental findings with molecular dynamics (MD) simulations and finite element modeling (FEM), valuable insights for material design, process optimization, and performance enhancement in CNM-based AM are provided to address ongoing challenges.
36 MATERIALS SCIENCE↗
Cryogenic Model Materials
An overview and status of current activities seeking alternatives to 200 grade 18Ni Steel CVM alloy for cryogenic wind tunnel models is presented. Specific improvements in material selection have been researched including availability, strength, fracture toughness and potential for use in transonic wind tunnel testing. Potential benefits from utilizing damage tolerant life-prediction methods, recently developed fatigue crack growth codes and upgraded NDE methods are also investigated. Two candidate alloys are identified and accepted for cryogenic/transonic wind tunnel models and hardware.
Kimmel, W. M.↗
Data-driven material models for atomistic simulation
The central approximation made in classical molecular dynamics simulation of materials is the interatomic potential used to calculate the forces on the atoms. Great effort and ingenuity is required to construct viable functional forms and find accurate parametrizations for potentials using traditional approaches. Machine learning has emerged as an effective alternative approach to develop accurate and robust interatomic potentials. Beginning with a very general model form, the potential is learned directly from a database of electronic structure calculations and therefore can be viewed as a multiscale link between quantum and classical atomistic simulations. Risk of inaccurate extrapolation exists outside the narrow range of time and length scales where the two methods can be directly compared. Here, we use the spectral neighbor analysis potential (SNAP) and show how a fit can be produced with minimal interpolation errors which is also robust in extrapolating beyond training. To demonstrate the approach, we have developed a tungsten-beryllium potential suitable for the full range of binary compositions. Subsequently, large-scale molecular dynamics simulations were performed of high energy Be atom implantation onto the (001) surface of solid tungsten. The machine learned W-Be potential generates a population of implantation structures consistent with quantum calculations of defect formation energies. A very shallow ( < 2 nm ) average Be implantation depth is predicted which may explain ITER diverter degradation in the presence of beryllium.
36 MATERIALS SCIENCE↗
Workshop on uncertainty quantification and multiscale materials modeling
Abstract Not Provided
36 MATERIALS SCIENCE↗
ASC PEM Materials Modeling Overview: Focus on Predicting Response of Metals [Slides]
In general, the large / high rate deformation process involves high pressure and temperature, finite elasticity, phase change, localization, porosity initiation, porosity growth, and coalescence dominated by localized deformation. These physical events are statistical, coupled, occur over several length scales, and intimately involves the microstructure. Understanding these complex thermo-mechanical processes is critical to our ability to numerically represent and predict these deformation events. ASC Materials is generally responsible for the modeling of 12-15 different metallic materials subjected to a broad range of conditions. This talk discusses current approaches and unanswered questions in the areas of high rate strength, phase transformations, and damage and failure.
36 MATERIALS SCIENCE↗
Low-temperature domain-wall freezing and nonequilibrium dynamics in the transverse-field Ising model material CoNb 2 O 6
CoNb 2 O 6 is a rare realization of the transverse-field Ising model, making it a useful tool for studying both equilibrium and nonequilibrium many-body quantum physics. Despite a large body of work dedicated to characterizing this material, details of the ordered states in the presence of relatively weak transverse fields have not been discussed in detail. Here, we present a detailed study of CoNb 2 O 6 via ac susceptibility measurements in order to further characterize its low-temperature behavior in the presence of a transverse field. Specifically, we call attention to an unconventional freezing transition in zero field below T F = 1.2 K, occurring within the well-known commensurate antiferromagnetic (CAFM) state that onsets at T N2 = 1.9 K. We performed a series of transverse-field quenches into this frozen state, which resulted in a slowly relaxing susceptibility, χ' (t), that followed a logarithmic decay within the time range measured. Additionally, we discuss the frozen state in the context of the freezing of previously discussed “free” chains arising from domain walls between the four degenerate sublattices of the CAFM state. We also attempted to observe Kibble-Zurek scaling by quenching the transverse field into the frozen state at different rates. This produced a null result; the behavior can be fully explained by coarsening of domains over the time scale of the quenches. The absence of a clear Kibble-Zurek scaling is itself surprising, given the proposed ubiquity of the phenomenon for general second-order phase transitions.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Parameter Estimation for Viscoplastic Material Modeling
A key ingredient in the design of engineering components and structures under general thermomechanical loading is the use of mathematical constitutive models (e.g. in finite element analysis) capable of accurate representation of short and long term stress/deformation responses. In addition to the ever-increasing complexity of recent viscoplastic models of this type, they often also require a large number of material constants to describe a host of (anticipated) physical phenomena and complicated deformation mechanisms. In turn, the experimental characterization of these material parameters constitutes the major factor in the successful and effective utilization of any given constitutive model; i.e., the problem of constitutive parameter estimation from experimental measurements.
Saleeb, Atef F.↗
Materials Modeling for High-Performance Radiation Detectors
This project is in collaboration with SL13-FY13-237-PD05. This project aims to enable rational materials design for select high-payoff challenges in radiation detection materials by using stateof- the-art modeling techniques bridging multiple resolutions, in cooperation with experimental collaborations. Three specific high-impact challenges are addressed: design of contacts for TlBr detectors to stabilize response at room-temperature; design of host glass for large-volume, lowcost, high-performance scintillators; and determination of the electrical impacts of dislocation networks in CZT that limit the performance and usable single-crystal volume. The realization of these goals will impact a number of proliferation detection application areas, including improved-performance and reduced-cost handheld detectors for search- and contain-missions (TlBr and CZT), and large-volume, specially-shaped detectors for portal monitors, tracking detectors, and similar applications (glass scintillators). The direct products of this project will be design and process strategies to achieve improved materials, whose implementation will yield detector devices that meet mission objectives. An added value of this project will be further advancement of the state-of-the-art in materials design capabilities, which is broadly useful for future materials needs.
36 MATERIALS SCIENCE↗
DECT-MULTRA: Dual-Energy CT Image Decomposition With Learned Mixed Material Models and Efficient Clustering
Dual-energy computed tomography (DECT) imaging plays an important role in advanced imaging applications due to its material decomposition capability. Image-domain decomposition operates directly on CT images using linear matrix inversion, but the decomposed material images can be severely degraded by noise and artifacts. This paper proposes a new method dubbed DECT-MULTRA for image-domain DECT material decomposition that combines conventional penalized weighted-least squares (PWLS) estimation with regularization based on a mixed union of learned transforms (MULTRA) model. Our proposed approach pre-learns a union of common-material sparsifying transforms from patches extracted from all the basis materials, and a union of cross-material sparsifying transforms from multi-material patches. The common-material transforms capture the common properties among different material images, while the cross-material transforms capture the cross-dependencies. The proposed PWLS formulation is optimized efficiently by alternating between an image update step and a sparse coding and clustering step, with both of these steps having closed-form solutions. The effectiveness of our method is validated with both XCAT phantom and clinical head data. The results demonstrate that our proposed method provides superior material image quality and decomposition accuracy compared to other competing methods.
47 OTHER INSTRUMENTATION↗
Analog study of the dynamics of a nonlinear Maxwell model material.
Mechanical behavior of materials with temperature dependent viscosity, using electric analogy on nonlinear Maxwell model
Gruntfest, I. J.↗
Materials Modeling for High-Performance Radiation Detectors
Limitations in the performance of room-temperature gamma radiation detectors are intimately related to the properties of the materials used to fabricate them. In particular, atomic-scale defects in the materials that can depend on crystal growth or device processing conditions often ultimately determine the performance of a given detector. Understanding the origins and consequences of these various defects is essential to optimizing the materials and achieving maximum device performance (e.g., energy resolution and sensitivity). This project aims to enable rational materials design for select high-payoff challenges in radiation detection materials by using state-of-the-art modeling techniques bridging multiple resolutions. Three specific high-impact challenges are addressed: (i) design and optimization of electrical contact stacks for thallium bromide (TlBr) detectors to stabilize temporal response at room-temperature; (ii) design of host glass for large-volume, low-cost, high-performance glass scintillators; and (iii) determination of the electrical impacts of dislocation networks in cadmium zinc telluride (CZT) that limit its performance and usable single-crystal volume.
36 MATERIALS SCIENCE↗
Micro-scale Artificial Weave Generation Capabilities for Thermal Protection System Material Modeling
Thermal Protection System (TPS) modeling requires accurate representation and prediction of the thermomechanical behavior of ablative materials. State-of-the-art TPS materials such as Phenolic Impregnated Carbon Ablator (PICA) have a proven flight record and demonstrate exceptional capabilities for handling extreme aerothermal heating conditions. The constant push for lightweight materials that are flexible in their design and performance, and hence allow for a wide range of mission profiles, has led NASA over the past years to develop its Heatshield for Extreme Entry Environment Technology (HEEET). HEEET is based primarily on a dual layer woven carbon fiber architecture and the technology has successfully been tested in arc-jet facilities. These recent developments have sparked interest in the accurate micro-scale modeling of composite weave architectures, to predict the structural response of macro-scale heatshields upon atmospheric entry. This effort can be extended to incorporate in-depth failure mechanics analyses as a result of local thermal gradients or high-velocity particle impact.
Visser, Sander J.↗
Particle-Phase Chemistry of Secondary Organic Material: Modeled Compared to Measured O:C and H:C Elemental Ratios Provide Constraints
Chemical mechanisms for the production of secondary organic material (SOM) are developed in focused laboratory studies but widely used in the complex modeling context of the atmosphere. Given this extrapolation, a stringent testing of the mechanisms is important. In addition to particle mass yield as a typical standard for model-measurement comparison, particle composition expressed as O:C and H:C elemental ratios can serve as a higher dimensional constraint. A paradigm for doing so is developed herein for SOM production from a C5 C10 C15 terpene sequence, namely isoprene, R-pinene, and β-caryopyhllene. The model MCM-SIMPOL is introduced based on the Master Chemical Mechanism (MCM v3.2) and a group contribution method for vapor pressures (SIMPOL). The O:C and H:C ratios of the SOM are measured using an Aerosol Mass Spectrometer (AMS). Detailed SOM-specific AMS calibrations for the organic contribution to the H2Oþ and COþ ions indicate that published O:C and H:C ratios for SOM are systematically too low. Overall, the measurement-model gap was small for particle mass yield but significant for particle-average elemental composition. The implication is that a key chemical pathway is missing from the chemical mechanism. The data can be explained by the particle-phase homolytic decomposition of organic hydroperoxides and subsequent alkyl-radical-promoted oligomerization.
54 ENVIRONMENTAL SCIENCES↗