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

Abstract

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: 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.

Segmentation, tracking, and sub-cellular feature extraction in 3D time-lapse images

“…The proposed segmentation method is implemented as a computational module in BisQue 29 , 30 . Users can run the CellECT2.0 module using the following steps: (1) Navigate to BisQue on their web browser and create an account, (2) Upload their own data in TIFF format or use suggested example dataset, (3) Select an uploaded TIFF image or use our example, (4) Set hyper parameters of the module (default value to run Dataset 1) and select Run and the BisQue service will compute the segmentation results and display it in the browser.…”

Three-dimensional Analysis and Reconstruction of Additively Manufactured Materials in the Cloud-Based BisQue Infrastructure

“…Capabilities for raw data storage, reconstruction, and analysis of 3D microstructures discussed here are also useful because they can be combined with tools for predicting microstructure-sensitive properties. Such tools which may include analytic bounds (e.g., Voigt/Reuss) or emerging data-driven models for microstructure-property relationships [50] can also be incorporated into BisQue, which is discussed in detail elsewhere [51].…”

Statistically equivalent representative volume elements (SERVE) for material behaviour analysis and multiscale modelling

“…Today, a variety of open-source and other software tools, such as Neper [104], DREAM.3D [31,[105][106][107][108] and others [109][110][111][112][113] are available as digital microstructure builders. Further, various other polycrystalline or polyphase microstructure generators have been proposed in [78,89,106,[114][115][116][117][118][119][120][121][122][123][124][125].…”