3D printing, or additive manufacturing, of metals uses a direct energy source, such as a laser or electron beam, to alloy powders, but has succeeded for only a few metals. Often, large columnar grains and cracks are generated during the solidification stage. In this paper, John Martin et al. confront this problem for aerospace-grade aluminium alloys […]
Building on these efforts, new tools are being developed to improve data curation, such as the Materials Data Curation System,70 Materials Commons,71 and the Citrine platform.55 Chance and Paul72 outline how to connect the wide variety of data sets and tools using a semantic web infrastructure. Kalidindi, S. R., Brough, D. B., Li, S., Cecen, A., […]
In the current implementation, SS-AutoPhase (semi-supervised AutoPhase) was used to phase map 278 diffractograms from a FeGaPd “open-data” combinatorial thin-film library.[Citrine Informatics, Fe-Ga-Pd, Citrination, http://citrination.com] Bunn, J. K., Hu, J., & Hattrick-Simpers, J. R. (2016). Semi-Supervised Approach to Phase Identification from Combinatorial Sample Diffraction Patterns. JOM, 68(8), 2116-2125.
Manual attribution of crystallographic phases from high-throughput x-ray diffraction studies is an arduous task, and represents a rate-limiting step in high-throughput exploration of new materials. Here, we demonstrate a semi-supervised machine learning technique, SS-AutoPhase, which uses a two-step approach to identify automatically phases from diffraction data. First, clustering analysis is used to select a representative […]
To this end, a variety of materials-related databases and data repositories have been established, for example, the Materials Project,4 the Open Quantum Materials Database (OQMD),5 the NIST Materials Data Repository,6,7 NREL MatDB,8 NIMS MatNavi,9 Automatic-FLOW for Materials Discovery,10 Novel Materials Discovery (NoMaD) repository,11 Computational Materials Data Network,12 Citrine Informatics’ Citrination platform,13 and AiiDA.1 Blaiszik, B., Chard, […]
With increasingly strict data management requirements from funding agencies and institutions, expanding focus on the challenges of research replicability, and growing data sizes and heterogeneity, new data needs are emerging in the materials community. The materials data facility (MDF) operates two cloud-hosted services, data publication and data discovery, with features to promote open data sharing, […]
However, most materials property information remains scattered across multiple resources […] We note that Citrine Informatics (http://www.citrine.io) is one commercial entity that is attempting to centralize information collected from diverse sources (both experimental and computational). Such analyses are culminating in more general “recommender” systems that can suggest new compounds based on observed data. For example, Citrine Informatics has built […]
Materials data and model repositories include those at NIST, 66 Citrine Informatics’ system, 67 NanoHuB, 68 and the National Data Service’s Materials Data Facility. McDowell, D. L., & Kalidindi, S. R. (2016). The materials innovation ecosystem: A key enabler for the Materials Genome Initiative. MRS Bulletin, 41(04), 326-337.
Some of these challenges have solutions on the horizon of which the authors are aware of. For example, Citrine Informatics (citrination.com) has undertaken the task of developing an open access database that will provide data infrastructure for all materials properties, both calculated and measured experimentally. Seshadri, R., & Sparks, T. D. (2016). Perspective: Interactive material property databases through […]
One organization that has made significant progress in establishing a centralized data resource for materials scientists is Citrine Informatics, a company that specializes in applying data mining to materials discovery and optimization. Jain, A., Persson, K. A., & Ceder, G. (2016). Research Update: The materials genome initiative: Data sharing and the impact of collaborative ab initio databases. […]
