Deep learning-based inverse design of microstructured materials for optical optimization and thermal radiation control
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Jonathan Sullivan↗
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Results for “microstructure design”
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Application of Computational Methods to Design Ceramic Microstructure for Engineering Applications.
Abstract not provided.
Tikare, Veena↗
Guiding the Design of Heterogeneous Electrode Microstructures for Li-Ion Batteries: Microscopic Imaging, Predictive Modeling, and Machine Learning
Electrochemical and mechanical properties of lithium-ion battery materials are heavily dependent on their 3D microstructure characteristics. A quantitative understanding of the role played by stochastic microstructures is critical for the prediction of material properties and for guiding synthesis processes. Furthermore, tailoring microstructure morphology is also a viable way of achieving optimal electrochemical and mechanical performances of lithium-ion cells. To facilitate the establishment of microstructure-resolved modeling and design methods, a review covering spatially and temporally resolved imaging of microstructure and electrochemical phenomena, microstructure statistical characterization and stochastic reconstruction, microstructure-resolved modeling for property prediction, and machine learning for microstructure design is presented here. The perspectives on the unresolved challenges and opportunities in applying experimental data, modeling, and machine learning to improve the understanding of materials and identify paths toward enhanced performance of lithium-ion cells are presented.
25 ENERGY STORAGE↗
Adoption of image-driven machine learning for microstructure characterization and materials design: A Perspective
Microstructure characterization enables the development of structure-processing-property relationships critical to several research areas within the broad field of materials science, from alloy design to the assessment of corrosion resistance, and failure analysis. Conventional approaches to material characterization have relied on either qualitative inference by the human ex-pert or software applications that can extract high-level features from images, such as boundary segmentation, average grain diameter, etc. Such approaches rely heavily on subject matter expert user intervention and knowledge of what phases or more generally, what microstructural features, are of interest. The recent surge in the adoption of machine learning techniques to address problems in materials engineering has brought with it an increased interest and application of Image Driven Machine Learning (IDML) approaches. In this work, we review the applications of IDML to the field of materials characterization. A canonical hierarchy of stages is defined, which when put sequentially together completes an IDML study: problem definition, dataset building, model selection and training, model evaluation, and integration with existing instrumentation or simulation workflow. The studies reviewed in this work are analyzed from the perspective of each of these stages. Such a review permits agranular assessment of the field, for example the impact of IDML on materials characterization at the nanoscale, the size of a typical dataset required to train a semantic segmentation model on electron microscopy images, ubiquitousness of transfer learning in the domain, etc. Finally, we discuss the importance of interpretability and explainability in the field of IDML for materials characterization, and provide an overview of two emerging techniques in the field: semantic segmentation and generative adversarial networks.
Baskaran, Arun↗
Macro-scale Testing and Micromechanics Modeling of Fracture Behaviors for Boron Carbide Composites with Hierarchical Microstructures
With a unique combination of properties including high hardness, low density, wear and corrosion resistance, thermal stability, high neutron absorption, and semi-conductivity, boron carbide (B4C) is a candidate material for various engineering applications that involve extreme environment. The current applications of boron carbide, however, are limited by its intrinsic brittleness due to its strong covalent bonding. To toughen boron carbide, in this work hierarchical microstructure designs was used to provide multiple toughening mechanisms including crack deflection/bridging, micro-crack toughening, etc. Using field assisted sintering, B4C composites with hierarchical microstructure features including graphite platelets, micron and sub-micron sized TiB2 reinforcements were fabricated. The fracture toughness of fabricated B4C composites were previously measured at micro-scale using micro-indentation followed by post-testing microstructure inspection. However, questions including whether the fracture toughness enhancement measured at micro-scale can translate to macro-scale mechanical properties, and what are the fundamental mechanisms behind observed fracture toughness enhancement, remain to be answered. In this study, the fabricated B4C composites were tested using standardized four-point bending method to obtain fracture toughness at macro-scale. In addition, micromechanics modeling was conducted using MAC/GMC code and crack-band model to study the effect of residual stress and weak interphases on fracture behaviors of B4C composites reinforced with TiB2 particles. Through standardized four-point bending tests, fracture toughness enhancements up to 2.85, 3.32, and 3.65 MPa∙m(exp 1/2) (from 2.38 MPa∙m(exp 1/2)) were achieved for B4C composites with graphite platelets addition (micro/nano B4C), with TiB2 formation (micro B4C-TiB2), and with both graphite and TiB2 addition (micro/nano B4C-TiB2) respectively. Micromechanics modeling indicated that introduction of thermal residual stress and weak interphases caused enhanced micro-cracking behavior and resulted in the observed fracture toughness enhancement. These results furthered understanding about the mechanical behaviors at macro-scale and the mechanisms behind observed fracture toughness enhancement for B4C composites with hierarchical microstructures and can provide reference data for the future design of B4C composites with optimized microstructures for further fracture toughness enhancement.
Boron Carbide↗
Multi-Objective Optimization of a Wrought Magnesium Alloy for High Strength and Ductility
An optimization technique is coupled with crystal plasticity based finite element (CPFE) computations to aid the microstructural design of a wrought magnesium alloy for improved strength and ductility. The initial microstructure consists of a collection of sub-micron sized grains containing deformation twins. The variables used in the simulations are crystallographic texture, and twin spacing within the grains. It is assumed that plastic deformation occurs mainly by dislocation slip on two sets of slip systems classified as hard and soft modes. The hard modes are those slip systems that are inclined to the twin planes and the soft mode consists of dislocation glide along the twin plane. The CPFE code calculates the stress-strain response of the microstructure as a function of the microstructural parameters and the length-scale of the features. A failure criterion based on a critical shear strain and a critical hydrostatic stress is used to define ductility. The optimization is based on the sequential generation of an initial population defined by the texture and twin spacing variables. The CPFE code and the optimizer are coupled in parallel so that new generations are created and analyzed dynamically. In each successive generation, microstructures that satisfy at least 90% of the mean strength and mean ductility in the current generation are retained. Multiple generation runs based on the above procedure are carried out in order to obtain maximum strength-ductility combinations. Finally, the implications of the computations for the design of a wrought magnesium alloy are discussed. Research sponsored by the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the U. S. Department of Energy.
36 MATERIALS SCIENCE↗
Multi-Objective Optimization of a Wrought Magnesium Alloy for High Strength and Ductility
An optimization technique is coupled with crystal plasticity based finite element (CPFE) computations to aid the microstructural design of a wrought magnesium alloy for improved strength and ductility. The initial microstructure consists of a collection of sub-micron sized grains containing deformation twins. The variables used in the simulations are crystallographic texture, and twin spacing within the grains. It is assumed that plastic deformation occurs mainly by dislocation slip on two sets of slip systems classified as hard and soft modes. The hard modes are those slip systems that are inclined to the twin planes and the soft mode consists of dislocation glide along the twin plane. The CPFE code calculates the stress-strain response of the microstructure as a function of the microstructural parameters and the length-scale of the features. A failure criterion based on a critical shear strain and a critical hydrostatic stress is used to define ductility. The optimization is based on the sequential generation of an initial population defined by the texture and twin spacing variables. The CPFE code and the optimizer are coupled in parallel so that new generations are created and analyzed dynamically. In each successive generation, microstructures that satisfy at least 90% of the mean strength and mean ductility in the current generation are retained. Multiple generation runs based on the above procedure are carried out in order to obtain maximum strength-ductility combinations. The implications of the computations for the design of a wrought magnesium alloy are discussed. Research sponsored by the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the U. S. Department of Energy.
Radhakrishnan, Balasubramaniam↗
Fundamental Processes of Coupled Radiation Damage and Mechanical Behavior in Nuclear Fuel Materials for High Temperature Reactors
The objective of this work has been to elucidate the relationship among microstructure, radiation damage and mechanical properties for nuclear fuel materials. As representative nuclear materials, we have taken an hcp metal (Mg as a generic metal, and Ti alloys for fast reactors) and UO2 (representing fuel). The degradation of the thermo-mechanical behavior of nuclear fuels under irradiation, both the fissionable material itself and its cladding, is a longstanding issue of critical importance to the nuclear industry. There are experimental indications that nanocrystalline metals and ceramics may be more resistant to radiation damage than their coarse-grained counterparts. The objective of this project look at the effect of microstructure on radiation damage and mechanical behavior in these materials. The approach to be taken was state-of-the-art, large-scale atomic-level simulation. This systematic simulation program of the effects of irradiation on the structure and mechanical properties of polycrystalline Ti and UO2 identified radiation damage mechanisms. Moreover, it will provided important insights into behavior that can be expected in nanocrystalline microstructures and, by extension, nanocomposites. The fundamental insights from this work can be expected to help in the design microstructures that are less susceptible to radiation damage and thermomechanical degradation.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Designing active particles for colloidal microstructure manipulation via strain field alchemy
We report a novel method for designing active interstitials capable of driving colloidal dislocations to glide.
VanSaders, Bryan↗
Physically constrained 3D diffusion for inverse design of fiber-reinforced polymer composite materials
Designing fiber-reinforced polymer composites (FRPCs) with a tailored nonlinear stress-strain response is crucial for applications such as energy absorption in crash structures, flexible robotics, and impact-resistant protective gear. However, the inherent complexities of composite materials and the multitude of parameters involved, render traditional design and optimization methods inadequate for achieving effective inverse design of composites. In this paper, we present an AI-based inverse design framework that effectively and efficiently generates FRPCs with targeted nonlinear stress-strain responses. We introduce a physically constrained diffusion model (PC3D_Diffusion) capable of managing the complexities of composite materials and producing detailed, high-quality designs. We propose a loss-guided, learning-free approach to generate physically feasible microstructure designs by explicitly enforcing physical constraints during the generation process. For training purposes, 1.35 million FRPC samples were created, and their corresponding stress-strain curves were computed using established physics-based computational models. The results show that PC3D_Diffusion consistently generates high-quality designs with tailored mechanical behaviors, while guaranteeing compliance with the physical constraints. PC3D_Diffusion advances FRPC inverse design and may facilitate the inverse design of other 3D materials, offering potential applications in industries reliant on materials with custom mechanical properties.
Xu, Pei [Clemson Univ., SC (United States)]↗
Mesoscale Modeling Approach for Quantifying Microstructure-Aware Micromechanical Responses in Metal Hydrides
Metal hydrides can undergo significant volume changes upon hydrogen uptake and release, which induce a mechanical response that depends not only on the evolving hydrogen composition but also on the microstructure. We present a comprehensive mesoscale modeling framework based on microelasticity theory to quantify the micromechanical responses of metal hydrides, specifically focusing on a hydrogenating polycrystalline MgH 2x particle within a host material as a model micromechanical system. Utilizing digitally generated realistic microstructures and density-functional-theory-derived parameters, we analyzed highly nonuniform local stress profiles in the polycrystalline hydrides under the clamping force exerted by the host during hydrogenation. Our framework also allows us to predict the corresponding strain energy accumulation and mechanical hot spots formation in the hydrides, highlighting their roles in thermodynamic destabilization and mechanical failure, respectively. Through extensive parametric simulations, we further quantified the influence of interface type, crystallinity, grain size, loading ratio, and host stiffness, providing practical guidance for optimizing microstructural design and host material selection. This proposed approach is broadly applicable to micromechanical systems with complex microstructural features involving chemical reaction- and/or phase-transformation-induced deformation.
36 MATERIALS SCIENCE↗
Towards inverse microstructure-centered materials design using generative phase-field modeling and deep variational autoencoders
The field of Integrated Computational Materials Engineering (ICME) combines a broad range of methods to study materials’ responses over a spectrum of length scales. A relatively unexplored aspect of microstructure-sensitive materials design is uncertainty propagation and quantification (UP/UQ) of materials’ microstructure, as well as establishing process-structure–property (PSP) relationships for inverse material design. In this study, an efficient UP technique built on the idea of changing probability measures and a deep generative unsupervised representative machine learning method for microstructure-based design of thermal conductivity of materials is proposed. Probability measures are used to represent microstructure space, and Wasserstein metrics are used to test the efficiency of the UP method. By using deep Variational AutoEncoder (VAE), we identify the correlations between the material/process parameters and the thermal conductivity of heterogeneous dual-phase microstructures. Through high-throughput screening, UP, and the deep-generative VAE method, PSP relationships that are too complex can be revealed by exploiting the materials’ design space with an emphasis on microstructures. As a last point, we demonstrate generative machine learning serves as a useful tool for inverse microstructure-centered materials design, and we demonstrate this by examining the inverse design of thermal conductivity in nano-structured materials. Here, the results reveal the effects of morphology, volume fraction, characteristic length scale, and the individual thermal diffusivity of phases on the thermal conductivity of dual-phase alloys. Our findings emphasize the advantages of high-throughput phase-field modeling and generative deep learning for linking PSP and inverse microstructure-centered materials design.
36 MATERIALS SCIENCE↗
Microstructural and microchemical studies of phase stability in V-O solid solution
Over the last couple of decades vanadium and V-based alloys have received significant attention as a potential structural material for fusion power applications because of their favourable mechanical properties under irradiation and at elevated temperatures. They are also considered as the advanced options of storage materials for hydrogen and its isotopes. However, the higher affinity of V for O, C and N poses critical challenges in its engineering applications since they lead to degradation of mechanical properties. They can further interact with the matrix to produce metallic oxy-carbo-nitride precipitates. To a certain limit, these precipitates are beneficial and can be exploited to enhance the mechanical behaviour of the alloy through suitable microstructural design. However, this requires a prior knowledge of the interaction between the alloy and the impurity solutes. In the present work vanadium specific experiments have been designed and carried out to bring out the V-interstitial solute interaction by charging oxygen in the near surface region of vanadium. Microstructural and microchemical behaviour of the V-O solid solution has been studied through HRTEM (high resolution transmission electron microscopy) and HAADF (high angle annular dark field) coupled with EELS. Quantitative electron microscopy has been carried out to study structural modification of the alloy in atomic level caused by O charging. - Highlights: •Controlled experiments were carried out in pulsed laser ablation set-up to promote V-O interaction. • As a consequence of O dissolution, V transformed into a bct structure which is otherwise a bcc structure. •In V-O solid solution, dissolved O in the V matrix introduces significant amount of lattice strain. • Present work can be extended for introducing interstitial O in other pure transition metals and their alloys.
36 MATERIALS SCIENCE↗
Spectral and Angular Dependences of the Efficiency of Three-Layer Relief-Phase Diffraction Elements of the IR Range
Within the framework of the scalar and rigorous diffraction theory based on solving a system of Maxwell equations, saw-tooth relief-phase three-layer microstructures designed for the work with polychromatic radiation of the mid-IR range (3 ≤ λ ≤ 5 μm) are studied. The series of effective microstructures composed of different optical materials is extended, and the depths of their reliefs are minimized. The series is extended, in particular, owing to microstructures in which Fresnel losses for reflection from both reliefs do not exceed 10%. This factor, in combination with the relatively low total relief depth, can turn out to be decisive when choosing one or another microstructure for solving a specific scientific or technological problem.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Engineering Nanocolumnar Defect Configurations for Optimized Vortex Pinning in High Temperature Superconducting Nanocomposite Wires
High temperature superconducting (HTS), coated conductor wires based on nanocomposite films containing self-assembled, insulating BaZrO3 (BZO) nanocolumnar defects have previously been reported to exhibit enhanced vortex pinning. Here, we report on microstructural design via control of BZO nanocolumns density in YBa2Cu3O7- (YBCO)+BZO nancomposite films to achieve the highest critical current density, Jc(H, ,T). X-ray diffraction and microstructural examination shows increasing number density of epitaxial BZO nanocolumns in the highly cube-textured YBCO matrix with increasing nominal BZO additions. Transport property measurement reveals that an increase in BZO content upto 4 vol% is required to sustain the highest pinning and Jc performance as the magnetic field increases. By growing thicker, single-layer nanocomposite films (~4 m) with controlled density of BZO columnar defects, the critical current (Ic) of ~1000 A/cm at 77 K, self-field and the minimum Ic of 455 A/cm at 65 K and 3 T for all magnetic field orientations were obtained. This is the highest Ic reported to date for films on metallic templates which are the basis for the 2nd generation, coated conductor-based HTS wires.
critical current density↗
Deformation behavior of metallic glass composites and plasticity accommodation at microstructural length-scales
Bulk metallic glass matrix composites represent a unique microstructural design strategy for overcoming the strength/ductility trade-off in structural alloys. Site-specific mechanical behavior of a Ti-based bulk metallic glass composite was evaluated at microstructural length-scale. The micro-pillars on the amorphous matrix showed on average a yield point of 1.9 GPa followed by serrated plastic deformation characteristic of shear banding. In contrast, micro-pillars on the crystalline dendritic phase showed a much lower yield strength of 0.72 GPa on average for the four different crystallographic orientations chosen, smooth plastic flow with no recognizable load burst, and stable strain-hardening after yield point. A number of micro-pillars were made at the interface between the glassy matrix and the crystalline dendrite. The mixed micro-pillars showed homogeneous deformation and greater plasticity which was attributed to their smaller stored elastic energy. Shear bands initiated in the amorphous matrix were arrested by the crystalline dendrite, which accommodated plasticity through slip bands and dislocations pile-ups. The interface remained intact after plastic deformation, with no observable signs of devitrification in the amorphous phase.
Bulk metallic glass matrix composite, Micro-pillar↗
Computationally Guided Development of Components for High Energy Density Solid-State Lithium-Sulfur Batteries
All electric vertical take-off and landing vehicles (eVTOL) for urban air mobility (UAM) concepts face numerous challenging technical barriers before their introduction into the consumer marketplace. The most challenging of these technical barriers to overcome is developing an energy storage system capable of meeting the rigorous aerospace safety and performance criteria1. The performance metrics for eVTOL craft, such as specific energy, specific power, and safety, exceed those of electric automobiles by a factor of two to four. Current state-of-the-art (SOA) lithium-ion batteries are incapable of meeting the key performance criteria of energy and safety for eVTOL. Therefore, next generation advanced chemistries and designs must be developed to meet required performance metrics for electric aviation2. Beyond lithium-ion chemistries, such as lithium-sulfur, show promise in their high energy, while limitations exist in their power and cyclability due to low electrical conductivity and high intermediate solubility in organic liquid electrolytes. Several strategies to overcome the low electrical conductivity involve the use of selenium as a dopant in the active sulfur material, along with the incorporation of 2-dimensional electron-conducting holey-graphene to improve the composite cathodes electronic conductivity. Furthermore, combining this chemistry with a solid-electrolyte avoids the components’ dissolution issues3. However, the development of composite solid-state cathodes is non-trivial as several components must be intimately mixed so that the active component has sufficient access to both electrons and lithium ions to undergo full electrochemical conversion. Mathematical modeling of battery components can assist experimental design through a robust and rigorous combination of computational modeling techniques covering multiple length scales. The objective is to leverage modern computational materials methods combined with battery multiphysics tools to develop radically advanced compatible cathode and electrolyte materials, build and test solid state lithium-sulfur cells and packs. A NASA-based cross-organizational team of high-powered experts combined integrated computational predictive modeling, fundamental chemistry analysis, advanced material science, and battery cell development to tackle this very challenging, multidisciplinary problem. This presentation will show a multiscale computational modeling approach that has produced a novel particle dynamics method called Solid Electrolyte Sphere Approximation Model (SESAM). SESAM modeling targets the 1-10 µm scale structures and provides electromechanical and grain interactions for predictive design guidelines for the manufacturing of solid-state components. Parameters such as particle size and volume fraction of the constituent materials were modeled and experimentally fabricated to optimize electrochemical performance through improved microstructure design. Experimental feedback was provided through ionic and electronic conductivity assessment and structural analysis of developed materials and cell components.
battery↗
Linking sintering stresses to nano modification in the microstructure of BaLa4Ti4O15 by transmission electron microscopy
Highlights: • by systematic HRTEM studies we link the microstructural changes in BLT ceramics to the external pressure applied during sintering; • we developed a mechanistic understanding of the role of stress in sintering; • external stresses during sintering affect the microstructural development at the nanoscale level, being responsible for the creation of higher defective bulk and grain boundary regions; • observation of high density of stacking faults (which generally arrises due to the Ti vacancy layers) indicates that the applied stress is modifying the stacking of AO{sub 3} layers of BLT which in turn creates Ti vacancy layer and stacking faults; • stress assisted sintering has, therefore, the potential to be used as a tool to design microstructures and achieve tailored properties, as suggested from our previous results in BLT and BNT. - Abstract: High quality factor and a temperature stable resonant frequency make BaLa{sub 4}Ti{sub 4}O{sub 15} (BLT) ceramics attractive materials for microwave applications. Aiming to exploit the effects of external stresses on the development of textured and anisotropic microstructures to optimise MW properties, the influence of applied external pressure during sintering of BLT ceramics is analysed. HRTEM and geometric phase analysis (GPA) showed that stresses applied during sintering, trigger the nucleation and growth of faults hypothesised to be due to the errors in the AO{sub 3} layer (basal plane) stacking sequence of the hexagonal perovskite structure. The results reveal a strong correlation between the high concentration of structural defects and the development of anisotropic microstructures, which tune the properties of BLT. Stresses applied during sintering are therefore a promising tool to design material properties.
36 MATERIALS SCIENCE↗