npj Computational Materials · 2021 · 38 citations · 34 references
ComputingCloud ComputingMaterials ScienceCloud InfrastructureData ManagementDatabasesCloud Data PlatformsData IntegrationData SharingGenomic DatabasesMaterials InformaticsResearch Data ManagementData DiscoveryData PresentationData-driven Materials DiscoveryDatabase InteroperabilityDiscovery ServicesMaterials Database DesignMaterials Informatics WorkflowsResearch Data Infrastructure
Abstract With scientific research in materials science becoming more data intensive and collaborative after the announcement of the Materials Genome Initiative, the need for modern data infrastructures that facilitate the sharing of materials data and analysis tools is compelling in the materials community. In this paper, we describe the challenges of developing such infrastructure and introduce an emerging architecture with high usability. We call this architecture the Materials Genome Engineering Databases (MGED). MGED provides cloud-hosted services with features to simplify the process of collecting datasets from diverse data providers, unify data representation forms with user-centered presentation data model, and accelerate data discovery with advanced search capabilities. MGED also provides a standard service management framework to enable finding and sharing of tools for analyzing and processing data. We describe MGED’s design, current status, and how MGED supports integrated management of shared data and services.
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Machine-learning-assisted materials discovery using failed experiments
Paul Raccuglia, Katherine C. Elbert, Philip Adler et al. · Nature · 2016 · 1.7K citations
Natural Sciences, Materials Engineering, Materials Science +12