@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 {1361,
	title = {Three-dimensional Analysis and Reconstruction of Additively Manufactured Materials in the Cloud-Based BisQue Infrastructure},
	journal = {Integrating Materials and Manufacturing Innovation},
	volume = {8},
	year = {2019},
	pages = {37{\textendash}51},
	abstract = {<p>
	A microstructure analytics and 3D reconstruction software package, DREAM.3D, was integrated as a module into a cloud-based platform, BisQue. Parallelization of DREAM.3D module executions and the ability to parameterize pipeline variables over a range of values have led to insights about the grain segmentation misorientation tolerance in TriBeam-collected 3D EBSD datasets of additively manufactured materials with complex anisotropic microstructures. Furthermore, a comparison in grain size measurements was made between standard 2D metallographic slices and 3D measures using BisQue\&$\#$39;s parallelized DREAM.3D module executions. The direction of cloud-based data infrastructure and the prospects for impact in material science are also discussed.
</p>
},
	keywords = {3D materials science, 3D metal printing, Additive manufacturing, Cloud infrastructure, Microstructure analytics, Tomography},
	isbn = {4019201900},
	issn = {21939772},
	doi = {10.1007/s40192-019-00126-7},
	author = {Polonsky, Andrew T. and Lang, Christian A. and Kvilekval, Kristian G. and Latypov, Marat I. and Echlin, McLean L.P. and Manjunath, B. S. and Pollock, Tresa M.}
}
