BisQue for 3D Materials Science in the Cloud: Microstructure–Property Linkages

TitleBisQue for 3D Materials Science in the Cloud: Microstructure–Property Linkages
Publication TypeJournal Article
Year of Publication2019
AuthorsLatypov MI, Khan A, Lang CA, Kvilekval K, Polonsky AT, Echlin MLLP, Beyerlein IJ, Manjunath B.S, Pollock TM
JournalIntegrating Materials and Manufacturing Innovation
ISBN Number4019201900
KeywordsCloud-based computing, Homogenization, Materials cyberinfrastructure, Reduced order models

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–structure–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–structure–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: 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.