Researchers at Sandia National Laboratories have integrated the GRANTA materials database with the MatCal calibration engine to calibrate material models from characterization data. GRANTA is gaining acceptance across the NNSA Tri-lab complex and is being populated with weapons-specific test data by Sandia experimentalists. To use that data to create material models for use by weapons systems analysts, MatCal has been enabled import calibration data and test conditions from GRANTA to quickly and reproducibly produce a calibrated set of parameters for a given constitutive model. The team is currently working to store the parameters characterizing material behavior in GRANTA to make them accessible by all weapons analysts.
To model and quantify the variability in plasticity and failure of additively manufactured metals due to imperfections in their microstructure, we have developed uncertainty quantification methodology based on pseudo marginal likelihood and embedded variability techniques. We account for both the porosity resolvable in computed tomography scans of the initial material and the sub-threshold distribution of voids through a physically motivated model. Calibration of the model indicates that the sub-threshold population of defects dominates the yield and failure response. Finally, the technique also allows us to quantify the distribution of material parameters connected to microstructural variability created by the manufacturing process, and, thereby, make assessments of material quality and process control.
Transition metal dichalcogenides (TMDs) such as MoX2 are known to undergo a structural phase transformation as well as a change in the electronic conductivity upon Li intercalation. These properties make them candidates for charge tunable ion-insertion materials that could be used in electro-chemical devices for neuromorphic computing applications. In this work we study the phase stability and electronic structure of Li-intercalated bilayer MoX2 with X=S, Se or Te. Using first-principles calculations in combination with classical and machine learning modeling approaches we find that the energy needed to stabilize the conductive phase decreases with increasing atomic mass of the chalcogen atom X. A similar decreasing trend is found in the threshold Li concentration where the structural phase transition takes place. While the electronic conductivity increases with increasing ion concentration at low concentrations, we do not observe a conductivity jump at the phase transition point.
Crystal plasticity theory is often employed to predict the mesoscopic states of polycrystalline metals, and is well-known to be costly to simulate. Using a neural network with convolutional layers encoding correlations in time and space, we were able to predict the evolution of the dominant component of the stress field given only the initial microstructure and external loading. In comparison to our recent work, we were able to predict not only the spatial average of the stress response but the evolution of the field itself. We show that the stress fields and their rates are in good agreement with the two dimensional crystal plasticity data and have no visible artifacts. Furthermore the distribution of stress throughout the elastic to fully plastic transition match the truth provided by held out crystal plasticity data. Lastly we demonstrate the efficacy of the trained model in material characterization and optimization tasks.
The mechanical properties of additively manufactured metals tend to show high variability, due largely to the stochastic nature of defect formation during the printing process. This study seeks to understand how automated high throughput testing can be utilized to understand the variable nature of additively manufactured metals at different print conditions, and to allow for statistically meaningful analysis. This is demonstrated by analyzing how different processing parameters, including laser power, scan velocity, and scan pattern, influence the tensile behavior of additively manufactured stainless steel 316L utilizing a newly developed automated test methodology. Microstructural characterization through computed tomography and electron backscatter diffraction is used to understand some of the observed trends in mechanical behavior. Specifically, grain size and morphology are shown to depend on processing parameters and influence the observed mechanical behavior. In the current study, laser-powder bed fusion, also known as selective laser melting or direct metal laser sintering, is shown to produce 316L over a wide processing range without substantial detrimental effect on the tensile properties. Ultimate tensile strengths above 600 MPa, which are greater than that for typical wrought annealed 316L with similar grain sizes, and elongations to failure greater than 40% were observed. It is demonstrated that this process has little sensitivity to minor intentional or unintentional variations in laser velocity and power.
In this work, we develop Gaussian process regression (GPR) models of isotropic hyperelastic material behavior. First, we consider the direct approach of modeling the components of the Cauchy stress tensor as a function of the components of the Finger stretch tensor in a Gaussian process. We then consider an improvement on this approach that embeds rotational invariance of the stress-stretch constitutive relation in the GPR representation. This approach requires fewer training examples and achieves higher accuracy while maintaining invariance to rotations exactly. Finally, we consider an approach that recovers the strain-energy density function and derives the stress tensor from this potential. Although the error of this model for predicting the stress tensor is higher, the strain-energy density is recovered with high accuracy from limited training data. The approaches presented here are examples of physics-informed machine learning. They go beyond purely data-driven approaches by embedding the physical system constraints directly into the Gaussian process representation of materials models.
In order to study the effects of Ni oxidation barriers on H diffusion in Zr, a Ni-Zr-H potential was developed based on an existing Ni-Zr potential. Using this and existing binary potentials H diffusion characteristics were calculated and some limited findings for the performance of Ni on Zr coatings are made.
This project has developed models of variability of performance to enable robust design and certification. Material variability originating from microstructure has significant effects on component behavior and creates uncertainty in material response. The outcomes of this project are uncertainty quantification (UQ) enabled analysis of material variability effects on performance and methods to evaluate the consequences of microstructural variability on material response in general. Material variability originating from heterogeneous microstructural features, such as grain and pore morphologies, has significant effects on component behavior and creates uncertainty around performance. Current engineering material models typically do not incorporate microstructural variability explicitly, rather functional forms are chosen based on intuition and parameters are selected to reflect mean behavior. Conversely, mesoscale models that capture the microstructural physics, and inherent variability, are impractical to utilize at the engineering scale. Therefore, current efforts ignore physical characteristics of systems that may be the predominant factors for quantifying system reliability. To address this gap we have developed explicit connections between models of microstructural variability and component/system performance. Our focus on variability of mechanical response due to grain and pore distributions enabled us to fully probe these influences on performance and develop a methodology to propagate input variability to output performance. This project is at the forefront of data-science and material modeling. We adapted and innovated from progressive techniques in machine learning and uncertainty quantification to develop a new, physically-based methodology to address the core issues of the Engineering Materials Reliability (EMR) research challenge in modeling constitutive response of materials with significant inherent variability and length-scales.
In this work, we examine metal electrode-ionomer electrolyte systems at high voltage (negative surface charge) and at high pH to assess factors that influence hydrogen production efficiency. We simulate the hydrogen evolution electrode interface investigated experimentally in the work of Bates et al. [J. Phys. Chem. C 119, 5467 (2015)] using a combination of first principles calculations and classical molecular dynamics. With this detailed molecular information, we explore the hypotheses posed in the work of Bates et al. In particular, we examine the response of the system to increased bias voltage and oxide coverage in terms of the potential profile, changes in solvation and species concentrations away from the electrode, surface concentrations, and orientation of water at reactive surface sites. We discuss this response in the context of hydrogen production.
The advent of fabrication techniques such as additive manufacturing has focused attention on the considerable variability of material response due to defects and other microstructural aspects. This variability motivates the development of an enhanced design methodology that incorporates inherent material variability to provide robust predictions of performance. In this work, we develop plasticity models capable of representing the distribution of mechanical responses observed in experiments using traditional plasticity models of the mean response and recently developed uncertainty quantification (UQ) techniques. To account for material response variability through variations in physical parameters, we adapt a recent Bayesian embedded modeling error calibration technique. We use Bayesian model selection to determine the most plausible of a variety of plasticity models and the optimal embedding of parameter variability. To expedite model selection, we develop an adaptive importance-sampling-based numerical integration scheme to compute the Bayesian model evidence. In conclusion, we demonstrate that the new framework provides predictive realizations that are superior to more traditional ones, and how these UQ techniques can be used in model selection and assessing the quality of calibrated physical parameters.
Glassy silicates are substantially weaker when in contact with aqueous electrolyte solutions than in vacuum due to chemical interactions with preexisting cracks. To investigate this silicate weakening phenomenon, classical molecular dynamics (MD) simulations of silica fracture were performed using the bond-order based, reactive force field ReaxFF. Four different environmental conditions were investigated: vacuum, water, and two salt solutions (1M NaCl, 1M NaOH) that form relatively acidic and basic solutions, respectively. Any aqueous environment weakens the silica, with NaOH additions resulting in the largest decreases in the effective fracture toughness (eKIC) of silica or the loading rate at which the fracture begins to propagate. The basic solution leads to higher surface deprotonation, narrower radius of curvature of the crack tip, and greater weakening of the silica, compared with the more acidic environment. The results from the two different electrolyte solutions correspond to phenomena observed in experiments and provide a unique atomistic insight into how anions alter the chemical-mechanical fracture response of silica.
