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.
Highlights
The progress of advanced technologies is critically dependent on the development of new materials satisfying ever increasing service requirements
The platform can be used by materials scientists for data storage and sharing and as cyberinfrastructure integrating materials science tools for tasks ranging from 3D microstructure reconstruction and analysis to prediction of properties based on 3D microstructure information
Predicted by the Materials Knowledge Systems (MKS) model implemented in BisQue and by classical Voigt/Reuss bounds, c mean effective yield strength values (MKS prediction and bounds) as a function of the MVE size with error bars indicating standard deviation development was exemplified through implementation of a data-driven model for prediction of effective yield strength of two-phase materials made of isotropically plastic constituents
Summary
The progress of advanced technologies is critically dependent on the development of new materials satisfying ever increasing service requirements. Accelerated materials development and implementation, in part, could clearly benefit from widely available platforms that (i) provide cyberinfrastructure for storage and management of materials data that facilitates sharing and collaborations across facilities and among research groups and (ii) integrate computational tools for essential tasks of materials research, such as microstructure analysis and establishing PSP relationships. Work on such platforms has already demonstrated their utility for various materials systems [13,14,15,16,17,18,19]. Modules that can be developed by users have access to the data provided by the Core Services and can write new data to the server derived (if any) as a result
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