@article {1351,
	title = {BisQue for 3D Materials Science in the Cloud: Microstructure{\textendash}Property Linkages},
	journal = {Integrating Materials and Manufacturing Innovation},
	volume = {8},
	year = {2019},
	pages = {52{\textendash}65},
	abstract = {<p>
	Accelerating the design and development of new advanced materials is one of the priorities in modern materials science. These efforts are critically dependent on the development of comprehensive materials cyberinfrastructures which enable efficient data storage, management, sharing, and collaboration as well as integration of computational tools that help establish processing\&ndash;structure\&ndash;property relationships. In this contribution, we present implementation of such computational tools into a cloud-based platform called BisQue (Kvilekval et al., Bioinformatics 26(4):554, 2010). We first describe the current state of BisQue as an open-source platform for multidisciplinary research in the cloud and its potential for 3D materials science. We then demonstrate how new computational tools, primarily aimed at processing\&ndash;structure\&ndash;property relationships, can be implemented into the system. Specifically, in this work, we develop a module for BisQue that enables microstructure-sensitive predictions of effective yield strength of two-phase materials. Towards this end, we present an implementation of a computationally efficient data-driven model into the BisQue platform. The new module is made available online (web address: https://bisque.ece.ucsb.edu/module_service/Composite_Strength/) and can be used from a web browser without any special software and with minimal computational requirements on the user end. The capabilities of the module for rapid property screening are demonstrated in case studies with two different methodologies based on datasets containing 3D microstructure information from (i) synthetic generation and (ii) sampling large 3D volumes obtained in experiments.
</p>
},
	keywords = {Cloud-based computing, Homogenization, Materials cyberinfrastructure, Reduced order models},
	isbn = {4019201900},
	issn = {21939772},
	doi = {10.1007/s40192-019-00128-5},
	author = {Latypov, Marat I. and Khan, Amil and Lang, Christian A. and Kvilekval, Kris and Polonsky, Andrew T. and Echlin, McLean L.P. and Beyerlein, Irene J. and Manjunath, B. S. and Pollock, Tresa M.}
}
@article {1426,
	title = {Bayesian inference of elastic properties with resonant ultrasound spectroscopy},
	journal = {The Journal of the Acoustical Society of America},
	volume = {143},
	year = {2018},
	pages = {71{\textendash}83},
	abstract = {<p>
	\&copy; 2018 Acoustical Society of America. Bayesian modeling and Hamiltonian Monte Carlo (HMC) are utilized to formulate a robust algorithm capable of simultaneously estimating anisotropic elastic properties and crystallographic orientation of a specimen from a list of measured resonance frequencies collected via Resonance Ultrasound Spectroscopy (RUS). Unlike typical optimization procedures which yield point estimates of the unknown parameters, computing a Bayesian posterior yields probability distributions for the unknown parameters, and HMC is an efficient way to compute this posterior. The algorithms described are demonstrated on RUS data collected from two parallelepiped specimens of structural metal alloys. First, the elastic constants for a specimen of fine-grain polycrystalline Ti-6Al-4 V with random crystallographic texture and isotropic elastic symmetry are estimated. Second, the elastic constants and crystallographic orientation for a single crystal Ni-based superalloy CMSX-4 specimen are accurately determined, using only measurements of the specimen geometry, mass, and resonance frequencies. The unique contributions of this paper are as follows: the application of HMC for sampling the Bayesian posterior of a probabilistic RUS model, and the procedure for simultaneous estimation of elastic constants and lattice-specimen misorientation. Compared to previous approaches these algorithms demonstrate superior convergence behavior, particularly when the initial parameterization is unknown, and enable substantially simplified experimental procedures.
</p>
},
	issn = {0001-4966},
	doi = {10.1121/1.5017840},
	author = {Bales, Ben and Petzold, Linda and Goodlet, Brent R. and Lenthe, William C. and Pollock, Tresa M.}
}
