Complex imaging of phase domains by deep neural networks
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Wu, Longlong↗
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Results for “computational materials science”
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A Continuum Dislocation Dynamics Crystal Plasticity Approach to Irradiated Body-Centered Cubic α-Iron
Radiation-induced embrittlement of reactor pressure vessel (RPV) steels can potentially limit the operating life of nuclear power plants. Over extended exposure to radiation doses, these body-centered cubic (BCC) irons demonstrate irradiation damage. Here, we present a continuum dislocation density (CDD) crystal plasticity model to capture the interaction among dislocations and self-interstitial atom (SIA) loops in α-iron. We demonstrate the importance of modeling cross slip using a combined stochastic Monte Carlo approach and the role of slip system strength anisotropy in capturing stochastic cross slip interactions. Through these captured interactions, the CDD crystal plasticity model can capture both the stress response and the physical evolution of dislocations on different slip system planes. Single-crystal verification experiments are used to calibrate the CDD crystal plasticity model, and a set of simplified polycrystalline simulations demonstrates the model’s ability to capture the stress response from tensile experiments on α-iron.
36 MATERIALS SCIENCE↗
Time-temperature history and input files for ExaCA v2.0 scaling, performance, and demonstration simulations
The files in this data repository are used in various sections of the manuscript "ExaCA v2.0: A versatile, scalable, and performance portable cellular automata application for additive manufacturing solidification" by Rolchigo et al. (DOI: 10.1016/j.commatsci.2025.113734). The README file references dataset numbers as given in the manuscript's Table 2, as well as the manuscript's relevant subsections.
36 MATERIALS SCIENCE↗
Multiple Degradation Mechanisms in Reinforced Concrete Structures, Modeling and Risk Analysis
The overarching goal of this project is to complement ongoing Department of Energy (DOE) Light Water Reactor Sustainability (LWRS)-funded Grizzly concrete modeling development efforts by providing improved multi-physics models for incorporation into the Grizzly and BlackBear codes. Objectives of this work identified at the outset of this project include: 1. Coupling between mechanical damage and transport processes. The mechanical damage will be characterized by nonlinear mechanical constitutive models appropriate for concrete used in nuclear power plants (NPPs). 2. Improved representation of the coupling among the transport process, building on the current moisture and thermal transport models in Grizzly. The parameters for coupling among heat and mass transport in the multi-physics model will be experimentally determined. 3. Coupling among different scale levels of concrete constituents, fine and coarse aggregates, cement paste, hydration products, and pore structure. 4. Probabilistic analysis of the random nature of the heterogeneous concrete structure and the environmental factors (ambient temperature and humidity) and their multiple effects on concrete deterioration. 5. Benchmark verification, validation and uncertainty quantification of the thermo-hygro- chemo-mechanical (THCM) concrete formulation. The end goal of this work was to provide a comprehensive and robust simulation capability that can be applied at the engineering scale for analysis of realistic deterioration scenarios in NPP concrete structures.
36 MATERIALS SCIENCE↗
Modular Linear Thermoviscoelastic Model
Time-dependent, viscoelastic responses of materials like polymers and glasses have long been studied. As such, a variety of models have been put forth to describe the behavior including simple rheological models (e.g. Maxwell, Kelvin), linear "fading memory" theories, and hereditary integral based linear thermal viscoelastic approaches as well as more recent nonlinear theories that are either integral, fictive temperature, or differential internal state variable based. The current work details a new LINEAR_THERMOVISCOELASTIC model that has been added to LAME. This formulation represents a viscoelastic theory that neglects some of the phenomenological details of the PEC/SPEC models in favor of efficiency and simplicity. Furthermore, this new model is a first step towards developing "modular" viscoelastic capabilities akin to those available with hardening descriptions for plasticity models in LAME. Specifically, multiple different (including user-defined) shift-factor forms are implemented with each being easily selected via parameter specification rather than requiring distinct material models.
36 MATERIALS SCIENCE↗
Open Source Visualization and Analysis Platform for 3D Reconstructions of Materials by Transmission Electron Microscopy
Three-dimensional characterization of materials at the nano- and meso-scale has become possible with transmission and scanning transmission electron microscopes (S/TEM). Its importance has extended to a wide class of nanomaterials such as hydrogen fuel cells, solar cells, industrial catalysts, new battery materials and semiconductor devices, as well as spanning high-tech industry, universities, and national labs. While capable instrumentation is abundant, this rapidly expanding demand for high-resolution tomography is bottlenecked by software that is instead tailored for lower-dose, biological applications and not optimized for higher-resolution materials applications. Existing tomography fails to utilize the chemical information provided by S/TEM spectrometers. To address this problem, this project delivered a scalable, fully functional, freely-distributable, open Source materials tomography package with a modern user interface that enables automated acquisition, alignment, and real-time reconstruction of raw tomography data, and provides advanced segmentation, three-dimensional chemical visualization and analysis optimized for materials applications. It has established an extendable framework capable of automation for high-throughput for the tomography of materials from data acquisition to visualization. Phase I and II delivered a full-featured, cross-platform, clean and integrated application for S/TEM materials tomography. It can read projection data from the microscope, with graphical tools for alignment of data, tomographic reconstruction, segmentation, and visualization of the reconstructed 3D volume. The entire pipeline can be saved to an XML state file, enabling fully reproducible data processing and analysis with a full Python environment for the development of custom algorithms, processing, and analysis requirements. Phase IIB extended the application to provide real-time tomographic reconstruction, "live updates" as data is processed, analysis of big data, and multi-channel tomography. Here, quantitative assessment of nanomaterials can occur as data is being recorded on a 3D visualization platform that accommodates additional information from multiple channels. We provide unmatched high-throughput tomography, where the complete tomographic pipeline from measurement to 3D visualization can occur rapidly. The project was extended to offer capabilities for micro-CT, X-ray, neutron, focused ion beam, atomic electron tomography and atom probe tomography. With over 600 transmission electron microscopes worldwide and approximately 50 coming online each year, the demand and impact of an open-source tomography tool is large. Significant opportunities exist in high-tech industry, universities, and national labs to enable or enhance three-dimensional imaging at the nanoscale and bring automated high-throughput approaches that will accelerate progress in materials characterization and metrology. The project supports a service based business model by enabling lab-specific acquisition and processing customization and integration-support and development that will be provided into Phase III and beyond.
36 MATERIALS SCIENCE↗
FY 2019 Site-Directed Research & Development (Annual Report Overview)
This overview of the Fiscal Year 2019 Site-Directed Research & Development program and the Nevada National Security Site contains information regarding the program’s history and growth, leadership, project selection process, present areas of research, highlights of significant accomplishments and awards, noteworthy people, and performance metrics.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
LANL MDMC Member Update: Summer 2020 [Slides]
An update is provided from LANL for the Material Data Management Consortium meeting.
36 MATERIALS SCIENCE↗
MIRaGE: Design Software for Metamaterials
Metamaterials are artificial optical structures that allow control of light in ways not found in, or offered by, naturally occurring materials. Sandia's Multiscale Inverse Rapid Group-theory for Engineered-metamaterials (MIRaGE) software, which won an R&D100 award in 2019, allows researchers to deterministically design and produce metamaterials with unique characteristics. MIRaGE also provides powerful autonomous optimization techniques for real-world performance in a rigorous, robust, and accurate manner.
36 MATERIALS SCIENCE↗
Operational and Mission Highlights: A Monthly Summary of Top Achievements
Highlights for the August 2020 newsletter are reported in the following areas: Nuclear Security; Science, Technology, and Engineering; Mission Operations; and Community Relations.
36 MATERIALS SCIENCE↗
A Method for Extrapolating CMM Data
This report provides a method for evaluating coordinate measuring machine (CMM) data and extrapolating the data to estimate the dimensions of a cylindrical component beyond the sampled measurements. The theoretical basis is provided, and a calculational method is presented. The report also provides detailed, formatted, printed, and plotted representations.
36 MATERIALS SCIENCE↗
Modeling a ring magnet in ALEGRA
We show here that Sandia's ALEGRA software can be used to model a permanent magnet in 2D and 3D, with accuracy matching that of the open-source commercial software FEMM. This is done by conducting simulations and experimental measurements for a commercial-grade N42 neodymium alloy ring magnet with a measured magnetic field strength of approximately 0.4 T in its immediate vicinity. Transient simulations using ALEGRA and static simulations using FEMM are conducted. Comparisons are made between simulations and measurements, and amongst the simulations, for sample locations in the steady-state magnetic field. The comparisons show that all models capture the data to within 7%. The FEMM and ALEGRA results agree to within approximately 2%. The most accurate solutions in ALEGRA are obtained using quadrilateral or hexahedral elements. In the case where iron shielding disks are included in the magnetized space, ALEGRA simulations are considerably more expensive because of the increased magnetic diffusion time, but FEMM and ALEGRA results are still in agreement. The magnetic field data are portable to other software interfaces using the Exodus file format.
36 MATERIALS SCIENCE↗
ALEGRA Parallel Scaling for Shock in a Heterogeneous Structure
We investigate the strong and weak parallel scaling performance of the ALEGRA multiphysics finite element program when solving a problem involving shock propagation through a heterogeneous material. We determine that ALEGRA scales well over a wide range of problem sizes, cores, and element sizes, and that scaling generally improves as the minimum element size in the mesh increases.
36 MATERIALS SCIENCE↗
Artificial Intelligence for Dynamic Phase Transitions in Materials [PowerPoint]
The presentation covers the following areas: emergent phenomena and non-equilibrium transitions in granular materials; materials science applications, including defects, interfaces and emergent phenomena; modeling with molecular dynamics simulations; principal component analysis (PCA); previous works; PCA applied to nonequilibrium transitions; PCA for varied quenched disorder; PCA incorporating particle velocity; and ending with a summary and impacts.
36 MATERIALS SCIENCE↗
Reification of latent microstructures: On supervised unsupervised and semi-supervised deep learning applications for microstructures in materials informatics
Machine learning (ML), including deep learning (DL), has become increasingly popular in the last few years due to its continually outstanding performance. In this context, we apply machine learning techniques to "learn" the microstructure using both supervised and unsupervised DL techniques. In particular, we focus (1) on the localization problem bridging (micro)structure (localized) property using supervised DL and (2) on the microstructure reconstruction problem in latent space using unsupervised DL. The goal of supervised and semi-supervised DL is to replace crystal plasticity finite element model (CPFEM) that maps from (micro)structure (localized) property, and implicitly the (micro)structure (homogenized) property relationships, while the goal of unsupervised DL is (1) to represent high-dimensional microstructure images in a non-linear low-dimensional manifold, and (2) to discover a way to interpolate microstructures via latent space associating with latent microstructure variables. At the heart of this report is the applications of several common DL architectures, including convolutional neural networks (CNN), autoencoder (AE), and generative adversarial network (GAN), to multiple microstructure datasets, and the quest of neural architecture search for optimal DL architectures.
36 MATERIALS SCIENCE↗
Library of Advanced Materials for Engineering (LAMÉ) 4.58
Accurate and efficient constitutive modeling remains a cornerstone issue for solid mechanics analysis. Over the years, the LAMÉ advanced material model library has grown to address this challenge by implementing models capable of describing material systems spanning soft polymers to stiff ceramics including both isotropic and anisotropic responses. Inelastic behaviors including (visco)plasticity, damage, and fracture have all incorporated for use in various analyses. This multitude of options and flexibility, however, comes at the cost of many capabilities, features, and responses and the ensuing complexity in the resulting implementation. Therefore, to enhance confidence and enable the utilization of the LAMÉ library in application, this effort seeks to document and verify the various models in the LAMÉ library. Specifically, the broader strategy, organization, and interface of the library itself is first presented. The physical theory, numerical implementation, and user guide for a large set of models is then discussed. Importantly, a number of verification tests are performed with each model to not only have confidence in the model itself but also highlight some important response characteristics and features that may be of interest to end-users. Finally, in looking ahead to the future, approaches to add material models to this library and further expand the capabilities are presented.
36 MATERIALS SCIENCE↗
Overview of the ALDX Common Modeling Framework [Slides]
The definition and benefits of using Common Modeling Framework (CMF) are described, along with shared material models.
36 MATERIALS SCIENCE↗
(U) ATCC-10 LSCI: Active learning for atomic plutonium potentials [Slides]
Abstract not provided.
36 MATERIALS SCIENCE↗