Publications

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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.

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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 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.

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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.

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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. 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.

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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.

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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.

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In this work we investigate the Orowan hypothesis, that decreases in surface energy due to surface adsorbates lead directly to lowered fracture toughness, at an atomic/molecular level. We employ a Lennard-Jones system with a slit crack and an infiltrating fluid, nominally with gold-water properties, and explore steric effects by varying the soft radius of fluid particles and the influence of surface energy/hydrophobicity via the solid–fluid binding energy. Using previously developed methods, we employ the J-integral to quantify the sensitivity of fracture toughness to the influence of the fluid on the crack tip, and exploit dimensionless scaling to discover universal trends in behavior.

Ionic liquids are a unique class of materials with several potential applications in electrochemical energy storage. When used in electrolytes, these highly coordinating solvents can influence device performance through their high viscosities and strong solvation behaviors. In this work, we explore the effects of pyrrolidinium cation structure and Li+ concentration on transport processes in ionic liquid electrolytes. We present correlated experimental measurements and molecular simulations of Li+ mobility and O2 diffusivity, and connect these results to dynamic molecular structural information and device performance. In the context of Li-O2/Li-air battery chemistries, we find that Li+ mobility is largely influenced by Li+-anion coordination, but that both Li+ and O2 diffusion may be affected by variations of the pyrrolidinium cation and Li+ concentration.

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Fracture toughness of silicates is reduced in aqueous environments due to water-silica interactions at the crack tip. To investigate this effect, classical molecular dynamics simulations using the bond-order-based reactive force field (ReaxFF) were used to simulate silica fracture. The chemical and mechanical aspects were separated by simulating fracture in (a) a vacuum with dynamic loading, (b) an aqueous environment with dynamic loading, and (c) an aqueous environment with static subcritical mechanical loading to track silica dissolution. The addition of water to silica fracture reduced the silica fracture toughness by ~25%, a trend consistent with experimentally reported results. Analysis of Si─O bonds in the process zone and calculations of dissipation energy associated with fracture indicated that water relaxes the entire process zone and not just the surface. Additionally, the crack tip sharpens during fracture in water and an increased number of microscopic propagation events occur. This results in earlier fracture in systems with increasing mechanical loading in aqueous conditions, despite the lack of significant silica dissolution. Therefore, the threshold for Si─O bond breakage has been lowered in the presence of water and the reduction in fracture toughness is due to structural and energetic changes in the silica, rather than specific dissolution events.