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All sources NASA NTRS DOE OSTI

DOE OSTI2023

Implementation and experimental validation of nonlocal damage in a large-strain elasto-viscoplastic FFT-based framework for predicting ductile fracture in 3D polycrystalline materials

Ductile materials, such as metal alloys, can undergo substantial deformation before failure. Additionally, these materials are usually of polycrystalline composition and exhibit strongly anisotropic behavior at small length scales. Previously developed fast Fourier transform (FFT)-based models can model ductile fracture of isotropic materials or the elastic–plastic behavior of anisotropic polycrystalline materials; however, there remains a need to couple both capabilities. This work extends a large-strain FFT-based crystal plasticity model to simulate ductile fracture of polycrystalline materials. In this work, a triaxiality-based continuum damage mechanics (CDM) formulation is incorporated into a large-strain elasto-viscoplastic FFT (LS-EVPFFT) framework. The CDM formulation is augmented with an integral-based nonlocal regularization approach that correctly handles gas-phase material necessary to model unconstrained surfaces. To validate the damage-enabled LS-EVPFFT framework, mesoscale copper tensile coupons were machined using microwire electrical discharge machining and experimentally characterized using electron backscatter diffraction. In-situ optical digital image correlation was performed during uniaxial testing to provide a side-by-side comparison of the experimental and computational strain fields and stress–strain responses. The damage-enabled LS-EVPFFT framework can simulate the complete macroscopic stress–strain response of ductile polycrystals to failure. The model reproduces necking behavior that qualitatively agrees with experimental observations. By leveraging the relatively low computational cost of the damage-enabled LS-EVPFFT framework, the framework presented here allows the ductile fracture response of 3D polycrystalline materials to be tractably predicted.

36 MATERIALS SCIENCE↗

NASA NTRS2000

Material Characterization for Ductile Fracture Prediction

The research summarized in this document provides valuable information for structural health evaluation of NASA infrastructure. Specifically, material properties are reported which will enable calibration of ductile fracture prediction methods for three high-toughness metallic materials and one aluminum alloy which can be found in various NASA facilities. The task of investigating these materials has also served to validate an overall methodology for ductile fracture prediction is currently being employed at NASA. In facilitating the ability to incorporate various materials into the prediction scheme, we have provided data to enable demonstration of the overall generality of the approach.

Hill, Michael R.↗

DOE OSTI2023

Artificial Intelligence Guided Studies of van der Waals Magnets

A materials informatics framework to explore a large number of candidate van der Waals (vdW) materials is developed. In particular, in this study a large space of monolayer transition metal halides is investigated by combining high-throughput density functional theory calculations and artificial intelligence (AI) to accelerate the discovery of stable materials and the prediction of their magnetic properties. Here, the formation energy is used as a proxy for chemical stability. Semi-supervised learning is harnessed to mitigate the challenges of sparsely labeled materials data in order to improve the performance of AI models. This approach creates avenues for the rapid discovery of chemically stable vdW magnets by leveraging the ability of AI to recognize patterns in data, to learn mathematical representations of materials from data and to predict materials properties. Using this approach, previously unexplored vdW magnetic materials with potential applications in data storage and spintronics are identified.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Multi-fidelity microstructure-induced uncertainty quantification by advanced Monte Carlo methods

Quantifying uncertainty associated with the microstructure variation of a material can be a computationally daunting task, especially when dealing with advanced constitutive models and fine mesh resolutions in the crystal plasticity finite element method (CPFEM). Numerous studies have been conducted regarding the sensitivity of material properties and performance to the mesh resolution and choice of constitutive model. However, a unified approach that accounts for various fidelity parameters, such as mesh resolutions, integration time-steps and constitutive models simultaneously is currently lacking. Further, this paper proposes a novel uncertainty quantification (UQ) approach for computing the properties and performance of homogenized materials using CPFEM, that exploits a hierarchy of approximations with different levels of fidelity. In particular, we illustrate how multi-level sampling methods, such as multi-level Monte Carlo (MLMC) and multi-index Monte Carlo (MIMC), can be applied to assess the impact of variations in the microstructure of polycrystalline materials on the predictions of homogenized materials properties. We show that by adaptively exploiting the fidelity hierarchy, we can significantly reduce the number of microstructures required to reach a certain prescribed accuracy. Finally, we show how our approach can be extended to a multi-fidelity framework, where we allow the underlying constitutive model to be chosen from either a phenomenological plasticity model or a dislocation-density-based model.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Toward non-Si electronics: From remote epitaxy to layer splitting of 2D materials for mixed dimensional heterostructures

The current electronics has been mainly dominated by Si-based devices due to their mature processing system and exceptional cost-effectiveness. However, next generation electronics needs novel functionalities that cannot be realized by Si because of intrinsic limitation of Si. Accordingly, demand for non-Si electronics has been getting substantially high. Unfortunately, current methodology requires extremely high cost for non-Si materials, which impedes the progress in developing the non-Si based electronics. Here, I will discuss about our group’s efforts to address this issue. Our team recently conceived a new crystalline growth, termed as “remote epitaxy”, which can copy/paste crystalline information from substrates remotely through graphene, thus generating single-crystalline films on graphene. As interfacial binding energy is attenuated by inserting graphene at interface, the single-crystalline films can be easily exfoliated from the slippery graphene surface. Also, the graphene-coated substrates can be, in principle, reused infinitely to produce single-crystalline films. Thus, the remote epitaxy can produce non-Si semiconductor films with unprecedented cost efficiency while allowing additional flexible device functionality required for current ubiquitous electronics. Next, I will discuss about a layer splitting technique which can be a potential solution to overcome the problem in obtaining large-scale and monolayer 2D materials. A 2D material-based heterostructure has been intensively studied because of its unique device functionalities and novel physics. However, it is extremely challenging to secure large-scale and monolayer 2D materials because of following issues: 1) poor scalability for laboratory fabrication processes of 2D heterostructures and 2) lack of well-defined control parameters for kinetics of 2D materials and predictable number of layers of 2D materials. To resolve this issue, we conceived a new approach called “layer-resolved splitting” which obtains multiple monolayer from multilayer 2D materials by controlling interfacial toughness contrast. As this method is versatile and universal, we can, in principle, apply to all 2D materials. We succeeded in having large-scale, monolayer 2D materials through our approach and, thereby 2D heterostructures were demonstrated for functional devices. Lastly, I would like to discuss opportunities of mixed-dimensional heterostructure demonstrated by remote epitaxy and layer-resolved splitting. As they produce freestanding 3D bulk films and 2D atomic layers, a new type of 3D/2D heterostructures can be realized where a new physics and new device architecture are revealed. Therefore, I believe that a new opportunity will be discovered through the mixed-dimensional heterostructures.

Bae, Sang-Hoon↗

DOE OSTI2025

Multiscale and Machine Learning Modeling for Process-informed Microstructure Prediction in Additively Manufactured Materials using MALAMUTE

The Advanced Materials and Manufacturing Technologies (AMMT) program under the Department of Energy Office of Nuclear Energy aims to develop and qualify additively manufactured materials for nuclear applications. One key challenge to this is the microstructural variability observed in the additively manufactured products and their impact on the properties and performance of the material in extreme environments. AMMT is using a combination of high-throughput experimental and modeling techniques to accelerate qualification. Conventionally, in-situ and ex-situ characterizations and testing are performed to correlate different aspects of the additive manufacturing process to the final product and its performance. However, adopting a trial-and-error approach to experimentally evaluate the vast range of process parameters required to capture microstructural variability is cost-prohibitive. Modeling and simulation provide a comparatively inexpensive way to understand and correlate the microstructural evolution to the processing conditions. The modeling and simulation work-packages within the AMMT program aims to use physics-based and machine learning models to develop a digital twin for additive manufacturing that can correlate the process conditions to the final product and establish a process-structure-property-performance (PSPP) correlation. The melting and subsequent solidification that occurs during the additive process is a complex phenomenon that requires multiscale multiphysics analysis. This work package focuses on understanding the role of process variabilities on the unique microstructural characteristics of additively manufactured materials. Microstructural features at the subgrain level, such as compositional micro-heterogeneity and dislocation cells, are of particular interest here since they can influence the creep properties and radiation performance. Idaho National Laboratory’s Multiphysics Object-Oriented Simulation Environment (MOOSE), specifically the MOOSE Application Library for Advanced Manufacturing UTilitiEs (MALAMUTE) software, provides an ideal platform for developing the multiphysics multiscale model to explore the intricacies of the microstructural evolution during the AM processes within a single framework. Furthermore, given that such full-fidelity simulations can be computationally intensive, reduced order models are necessary to explore the PSPP space for additively manufactured materials in an efficient, reliable, and cost-effective way. This work focuses on capturing the microstructural variabilities at the subgrain level that are often missing in the part-scale models. In fiscal year 2025, we significantly advanced upon our work in the last fiscal year, in terms of the predictive capabilities of the physics-based and ML models, by adding the capabilities to capture subgrain-level micro-segregation during solidification using phase-field model and to predict the time-dependent dynamics of the AM process through the MOGPAR model. The alloy solidification model in MOOSE incorporates the thermodynamic properties and free energy relevant to 316 stainless steel. The model demonstrates the Cr and Ni segregation that occurs during solidification, including that the rate of solidification. The microstructural evolution model is connected to the process conditions via the surrogate model developed in this work. This enables predictions of the final microstructure in conjunctions with the manufacturing process. This work supports AMMT's rapid qualification goals by laying the foundation for an efficient and cost-effective model establishing the PSPP correlation for AM. The generated microstructures and predicted micro-segregation can be used by other work packages under AMMT to evaluate the properties and environmental response of the material at the mesoscale. Thus, this work helps to identify the key microstructural features at the subgrain level that are significant in property and performance predictions of additively manufactured components. This work will also provide inputs to the large-scale process variability models to reevaluate and validate assumptions and simplifications made in the part-scale models. Furthermore, through active learning this work can help identify the data need from both modeling and experimental sides for development of a robust digital twin for additive manufacturing and accelerate the AMMT's qualification efforts.

36 - MATERIALS SCIENCE↗

DOE OSTI2022

Naturally-meaningful and efficient descriptors: machine learning of material properties based on robust one-shot ab initio descriptors

Establishing a data-driven pipeline for the discovery of novel materials requires the engineering of material features that can be feasibly calculated and can be applied to predict a material’s target properties. Here we propose a new class of descriptors for describing crystal structures, which we term Robust One-Shot Ab initio (ROSA) descriptors. ROSA is computationally cheap and is shown to accurately predict a range of material properties. These simple and intuitive class of descriptors are generated from the energetics of a material at a low level of theory using an incomplete ab initio calculation. We demonstrate how the incorporation of ROSA descriptors in ML-based property prediction leads to accurate predictions over a wide range of crystals, amorphized crystals, metal–organic frameworks and molecules. We believe that the low computational cost and ease of use of these descriptors will significantly improve ML-based predictions.

36 MATERIALS SCIENCE↗

DOE OSTI2021

First steps toward predicting corrosion behavior of structural materials in molten salts

To address the need for physics-based models to predict corrosion behavior of materials in molten salts, here we propose potential methods to aid in selection of optimum materials for structural components in molten salt powered technologies. In the present work, the role of alloy thermodynamics and kinetics on governing corrosion rates of Ni-based alloys will be discussed combining experimental and computational methods. A few strategies are presented to quantify corrosion rates of Ni-based materials isothermally exposed in purified KCl-MgCl 2 salts at 600° C-800° C. The influence of capsule material, potential corrosion products and role of alloy composition on the observed corrosion rates was discussed with coupled thermodynamic-kinetic models. Larger depths of corrosion attack were observed in alloy 230 specimens compared to other alloys under similar conditions was attributed to the much higher chemical activity of Cr in the alloy that results in a larger Cr chemical potential gradient.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Multiscale and Machine Learning Modeling for Process-informed Microstructure Prediction in Additively Manufactured Materials Using MALAMUTE

Advanced Materials and Manufacturing Technologies (AMMT) program under the Department of Energy Office of Nuclear Energy, aims to develop and qualify additively-manufactured materials for nuclear applications. The key challenges to these efforts are the microstructural variabilities observed on the AM products and their impact on the properties and performance of the material in extreme environments. AMMT is using a combination of high-through-put experimental and modeling techniques to accelerate the qualification efforts. Conventionally, in-situ and ex-situ characterizations and testing are performed to correlate different aspects of the AM process to the final product and its performance. However, adopting a trial-and-error approach to experimentally evaluate the vast range of process parameters required to capture the microstructural variabilities is cost-prohibitive. Modeling and simulation provide a comparatively inexpensive way to understand and correlate the microstructural evolution to the processing conditions. The modeling and simulation work-packages within the AMMT program aims to use physics-based and machine learning modeling capabilities to develop a digital twin for AM that can correlate the process conditions to the final product and establish a process-structure-property-performance (PSPP) correlation for AM materials. The melting and subsequent solidification that occurs during the AM process is a complex phenomenon that requires multiscale multiphysics analysis. Idaho National Laboratory’s (INL) Multiphysics Object-Oriented Simulation Environment (MOOSE), specifically the MOOSE Application Library for Advanced Manufacturing UTilitiEs (MALAMUTE) software, provides an ideal platform for developing the multiphysics multiscale model to explore the intricacies of the microstructural evolution during the AM processes within a single framework. Furthermore, given that such full-fidelity simulations can be computationally intensive, reduced order models are necessary to explore the PSPP space for AM materials in an efficient, reliable, and cost-effective way. This work package focuses on understanding the role of process variabilities on the various microstructural characteristics of the AM materials. Microstructures unique to AM materials, such as compositional micro-heterogeneity and dislocation cells, are of particular interest here since they can influence the creep properties and radiation performance. In fiscal year (FY) 24, we significantly advanced upon our work in the last fiscal year, both on physics-based and ML models. The alloy solidification model available in MOOSE has been extended to incorporate the thermodynamic properties and free energy relevant to 316SS. The model demonstrates the Cr segregation that occurs during solidifcation. It is demonstrated that rate of solidification and solute segregation is primarily influence by the cooling rate dictating the level of freezing. This work captures the microstructural variabilities at the subgrain level that are often missing in the part-scale models. With an aim to connect the microstructural evolution model to realistic process conditions, a reduced order model is developed for predicting the thermal conditions around meltpool from high-fidelity process simulations. Furthermore, machine learning approach is used to accelerate the temperature prediction during the AM process. In the following years, MALAMUTE will be used to connect different aspects of the models and quantitatively predict the microstructural evolution. The developed ML-based surrogate model will consider the process conditions as the input to predict the microstructural features in a cost-effective way. The generated microstructures can be used by other work packages under AMMT to evaluate the properties and environmental response of the material at the mesoscale. Thus, this work help identify the key microstructural features at the subgrain level that are significant in property/performance prediction of the AM products. This work will provide inputs to the large-scale process variability models to reevaluate and validate assumptions/simplifications made in the part-scale models. Furthermore, through active learning this work will help identify the data need from both modeling and experimental sides for development of a robust digital twin for AM.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Extracting Material Property Measurement Data from Scientific Articles

Machine learning-based prediction of material properties is often hampered by the lack of sufficiently large training datasets. The majority of such measurement data is embedded in scientific literature and the ability to automatically extract these data is essential to support the development of reliable property prediction methods. In this work, we describe a methodology for an automatic property extraction framework using material solubility as the target property. We create an annotated dataset containing tags for solubility-related entities using a combination of regular expressions and manual tagging. We then compare five entity recognition models leveraging both token-level and span-level architectures on the task of classifying solute names, solubility values, and solubility units. Additionally, we explore a novel pretraining approach that leverages automated chemical name and quantity extraction tools to generate large datasets that do not rely on intensive manual effort. Finally, we perform an analysis to identify the causes of classification errors.

Panapitiya, Gihan U.↗

NASA NTRS1985

Life prediction and constitutive models for engine hot section anisotropic materials program

The purpose is to develop life prediction models for coated anisotropic materials used in gas temperature airfoils. Two single crystal alloys and two coatings are now being tested. These include PWA 1480; Alloy 185; overlay coating, PWA 286; and aluminide coating, PWA 273. Constitutive models are also being developed for these materials to predict the plastic and creep strain histories of the materials in the lab tests and for actual design conditions. This nonlinear material behavior is particularily important for high temperature gas turbine applications and is basic to any life prediction system.

Swanson, G. A.↗

DOE OSTI2021

An Abrasive Wear Model of Knife Milling to Predict the Impact of Material Properties and Milling Parameters on Knife Edge Recession

A workable analytical abrasion model that relates critical knife-mill process parameters (geometry and rotational speed) to critical material attributes of inorganic mineral species in feedstock (density, size, and aspect ratio) and substrate (hardness and elastic modulus) was formulated to model wear of knives in knife-milling systems. Results of the model were compared to experimental observations of the edge recession of knives used in a knife mill marketed by Eberbach. Results showed good agreement between the predicted and measured shape of a worn knife and showed that a quality-by-design approach can be developed to predict component reliability based on scientific engineering principles in lieu of trial-and-error approaches.

36 MATERIALS SCIENCE↗

NASA NTRS1983

Creep fatigue life prediction for engine hot section materials (isotropic)

The activities performed during the first year of the NASA HOST Program, Creep Fatigue Life Prediction for Engine Hot Section Materials (Isotropic), being conducted by Pratt & Whitney Aircraft are summarized. The program is a 5 year, two part effort aimed at improving the high temperature crack initiation prediction technology for gas turbine hot section components. Significant results of the program produced thus far are discussed. Cast B1900 + Hf and wrought IN 718 were selected as the base and alternate materials, respectively. A single heat of B1900 + Hf was obtained and test specimens fabricated. The material was characterized with respect to grain size, gamma prime size, carbide distribution, and dislocation density. Monotonic tensile and creep testing has shown engineering properties within anticipated scatter for this material. Examination of the tensile tests has shown a transition from inhomogeneous planar slip within the grains at lower temperatures to more homogeneous matrix deformation. Examination of the creep tests has shown a transgranular failure mode at 1400 F and an intergranular failure mode at 1600 F and 1800 F.

Moreno, V.↗

DOE OSTI2026

Machine-Learning-Driven Discovery of Water Splitting BaFe 2 O 4 and Human-in-the-Loop Improvement via Al-Substitution for Increased Thermal Stability

Thermochemical hydrogen (TCH) production offers a promising method for converting thermal energy into hydrogen fuel through heat-driven redox cycles of metal oxides. Here, in this work a defect graph neural network (dGNN) was used to predict oxygen vacancy formation energies ΔH V O combined with Materials Project predictions of oxygen chemical potential stability to screen candidate oxides via high-throughput database analysis. BaFe 2 O 4 was identified as a promising material for experimental validation based on its predicted ΔH V O , oxygen chemical potential stability range, and potential for tunable substitutions to improve thermal properties. Experimental validation using thermogravimetric analysis (TGA), stagnation flow reactor (SFR), X-ray diffraction (XRD), and electron microscopy confirmed positive water-splitting behavior but also revealed limitations in thermal stability under aggressive reduction conditions. To address this, a human-in-the-loop modification strategy was employed introducing Al substitution in BaFe 2–x Al x O 4 ; this modification improves thermal stability, alters the crystal structure and enhances overall performance. These results demonstrate a combined computational and experimental workflow in which machine learning accelerates identification of promising candidates, while targeted experimental design enables optimization of functional performance. This approach advances the development of robust, cost-effective TCH materials and highlights the importance of integrating data-driven discovery with human-guided materials design in paving the way for scalable hydrogen production technologies.

organic↗

NASA NTRS2006

Development of Design Analysis Methods for C/SiC Composite Structures

The stress-strain behavior at room temperature and at 1100 C (2000 F) was measured for two carbon-fiber-reinforced silicon carbide (C/SiC) composite materials: a two-dimensional plain-weave quasi-isotropic laminate and a three-dimensional angle-interlock woven composite. Micromechanics-based material models were developed for predicting the response properties of these two materials. The micromechanics based material models were calibrated by correlating the predicted material property values with the measured values. Four-point beam bending sub-element specimens were fabricated with these two fiber architectures and four-point bending tests were performed at room temperature and at 1100 C. Displacements and strains were measured at various locations along the beam and recorded as a function of load magnitude. The calibrated material models were used in concert with a nonlinear finite element solution to simulate the structural response of these two materials in the four-point beam bending tests. The structural response predicted by the nonlinear analysis method compares favorably with the measured response for both materials and for both test temperatures. Results show that the material models scale up fairly well from coupon to subcomponent level.

Sullivan, Roy M.↗

NASA NTRS2012

Effect of Surface Nonequilibrium Thermochemistry in Simulation of Carbon Based Ablators

This study demonstrates that coupling of a material thermal response code and a flow solver using finite-rate gas/surface interaction model provides time-accurate solutions for multidimensional ablation of carbon based charring ablators. The material thermal response code used in this study is the Two-dimensional Implicit Thermal Response and Ablation Program (TITAN), which predicts charring material thermal response and shape change on hypersonic space vehicles. Its governing equations include total energy balance, pyrolysis gas momentum conservation, and a three-component decomposition model. The flow code solves the reacting Navier-Stokes equations using Data Parallel Line Relaxation (DPLR) method. Loose coupling between material response and flow codes is performed by solving the surface mass balance in DPLR and the surface energy balance in TITAN. Thus, the material surface recession is predicted by finite-rate gas/surface interaction boundary conditions implemented in DPLR, and the surface temperature and pyrolysis gas injection rate are computed in TITAN. Two sets of gas/surface interaction chemistry between air and carbon surface developed by Park and Zhluktov, respectively, are studied. Coupled fluid-material response analyses of stagnation tests conducted in NASA Ames Research Center arc-jet facilities are considered. The ablating material used in these arc-jet tests was a Phenolic Impregnated Carbon Ablator (PICA). Computational predictions of in-depth material thermal response and surface recession are compared with the experimental measurements for stagnation cold wall heat flux ranging from 107 to 1100 Watts per square centimeter.

Chen, Yih-Kang↗

DOE OSTI2020

Predicting the chemical reactivity of organic materials using a machine-learning approach

Stability and compatibility between chemical components are essential parameters that need to be considered in the selection of functional materials in configuring a system. In configuring devices such as batteries or solar cells, not only the functionality of individual constituting materials such as electrodes or electrolyte but also an appropriate combination of materials which do not undergo unwanted side reactions is critical in ensuring their reliable performance in long-term operation. While the universal theory that can predict the general chemical reactivity between materials is long awaited and has been the subject of studies with a rich history, traditional ways proposed to date have been mostly based on simple electronic properties of materials such as electronegativity, ionization energy, electron affinity and hardness/softness, and could be applied to only a small group of materials. Moreover, prediction has often been far from accurate and has failed to offer general implications; thus it was practically inadequate as a selection criterion from a large material database, i.e. data-driven material discovery. Herein, we propose a new model for predicting the general reactivity and chemical compatibility among a large number of organic materials, realized by a machine-learning approach. As a showcase, we demonstrate that our new implemented model successfully reproduces previous experimental results reported on side-reactions occurring in lithium–oxygen electrochemical cells. Furthermore, the mapping of chemical stability among more than 90 available electrolyte solvents and the representative redox mediators is realized by this approach, presenting an important guideline in the development of stable electrolyte/redox mediator couples for lithium–oxygen batteries.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1971

Metal alloy resistivity measurements at very low temperatures

High speed, automated system accurately measures to approximately one percent in three minutes. System identifies materials having constant thermal or electric conductivity, predicts new material properties, develops alloys in accordance with desired specifications, and develops nondestructive devices for measuring precipitation hardening.

Childs, G. E.↗