The Joint Automated Repository for Various Integrated Simulations (JARVIS) is an integrated infrastructure to accelerate materials discovery and design using density functional theory (DFT), classical force-fields (FF), and machine learning (ML) techniques. JARVIS is motivated by the Materials Genome Initiative (MGI) principles of developing open-access databases and tools to reduce the cost and development time of materials discovery, optimization, and deployment. The major features of JARVIS are: JARVIS-DFT, JARVIS-FF, JARVIS-ML, and JARVIS-tools. To date, JARVIS consists of ≈40,000 materials and ≈1 million calculated properties in JARVIS-DFT, ≈500 materials and ≈110 force-fields in JARVIS-FF, and ≈25 ML models for material-property predictions in JARVIS-ML, all of which are continuously expanding. JARVIS-tools provides scripts and workflows for running and analyzing various simulations. We compare our computational data to experiments or high-fidelity computational methods wherever applicable to evaluate error/uncertainty in predictions. In addition to the existing workflows, the infrastructure can support a wide variety of other technologically important applications as part of the data-driven materials design paradigm. The JARVIS datasets and tools are publicly available at the website: https://jarvis.nist.gov.
Experimental pole figures are an important input for microstructure homogenization models. In this paper, we derive an exact analytical formulation to quantify the uncertainties in homogenized properties due to uncertainty in the experimentally measured pole figures. The pole figures are acquired from a set of Ti-7Al alloy samples. These samples were obtained from the same process: by compressing a beta forged ingot at room temperature followed by annealing. The samples were taken from different regions of the original ingot, and this created variability in the resulting pole figures. The joint multivariate probability distributions of the computed orientation distribution function (ODF) is then found using the method of characteristic functions based on a Gaussian model of the pole figures. Engineering properties such as elastic modulus can be obtained by volume averaging over the ODF. We also show that uncertainty in elastic properties can be analytically obtained using direct transformation of random variables in the homogenization approach.
This paper addresses a two-step linear solution scheme to find an optimum metallic microstructure satisfying performance needs and manufacturability constraints. The microstructure is quantified using the orientation distribution function, which determines the volume densities of crystals that make up the polycrystal microstructure. The orientation distribution function of polycrystalline alloys is represented in a discrete form using finite elements, and the volume-averaged properties are computed. The first step of the solution approach identifies the orientation distribution functions that lead to the set of optimal engineering properties using linear programming. This step leads to multiple solutions, of which only a few can be manufactured using traditional processing routes such as rolling and forging. In the second step, textures from a given process are represented in a space of reduced basis coefficients called the process plane. This step involves generation of orthogonal basis functions for representing spatial variations of the orientation distribution functions during a given process using proper orthogonal decomposition. Multiple orientation distribution function solutions in step one are then projected onto these basis functions to identify which of the optimal textures are feasible through a given manufacturing process. This feasibility is determined with two approaches. The first approach finds the closest match to the orientation distribution function solutions in the material plane, whereas the second approach finds the closest match to a desired set of properties instead of the orientation distribution functions. The method is explained through an example of vibration tuning of a galfenol alloy, with the primary objective of maximizing the yield strength.
Microstructures have a significant effect on the performance of critical components in numerous aerospace metallic material applications. Examples include panels in airframes that are exposed to high temperatures and sensors used for vibration tuning. This paper addresses the techniques to optimize the microstructure design for polycrystalline metals. The microstructure is quantified with the orientation distribution function that determines the volume densities of crystals that make up the polycrystal microstructure. The orientation distribution function of polycrystalline alloys (e.g., hexagonal close-packed titanium) is represented in a discrete form, and the volumeaveraged properties are computed through the orientation distribution function. The optimization is performed using the space of all possible volume-averaged macroproperties (stiffness and thermal expansion). A direct linear solver is employed to find the optimal orientation distribution functions. The direct solver is capable of finding exact solutions even for problems with multiple or infinite solutions. It is first applied to the optimization of the panel-buckling problem. The objective of the buckling optimization problem is to find the best microstructure design that maximizes the critical buckling temperature. The optimum solution computed with this approach is found to be same as the optimum solution of a global approach that uses a genetic algorithm. The linear solver methodology is extended to plastic properties and applied to explore the design of a Galfenol beam microstructure for vibration tuning with a yielding objective. The design approach is shown to lead to multiple optimum solutions.
Electron backscatter diffraction (EBSD) scans are an important experimental input for microstructure generation and homogenization. Multiple EBSD scans can be used to sample the uncertainty in orientation distribution function (ODF), both point-to-point within a specimen as well as across multiple specimens that originate from the same manufacturing process. However, microstructure analysis methods typically employ only the mean values of the ODF to predict properties and the stochastic information is lost. In this work, we develop analytical methods to account for the uncertainty in the EBSD data during property analysis. To this end, we develop a linear smoothing scheme in the Rodrigues fundamental region to compute the ODF from the EBSD data. The joint multivariate probability distributions of the ODF are then modeled using a Gaussian assumption. We also compute the uncertainty in engineering properties that are obtained by homogenization. We show that uncertainty in non-linear properties can be analytically obtained using direct transformation of random variables in the homogenization approach.
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