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Beyond bulk single crystals: A data format for all materials structure–property–processing relationships

Published online by Cambridge University Press:  02 August 2016

Kyle Michel  and
Bryce Meredig
Kyle Michel
Affiliation:
Citrine Informatics, USA; [email protected]
Bryce Meredig
Affiliation:
Citrine Informatics, USA; [email protected]
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Abstract

Methods used in informatics require input data that are in a machine-readable, structured format. Materials data, in particular, can be exceedingly complex, so defining data formats to store any and all materials-related information is a daunting task. In this article, we discuss a hierarchical data structure used for storing materials data called the physical information file (PIF). The PIF is a flexible schema for storing the structure, processing history, and properties of materials, devices, and physical systems. In addition to a general discussion of the schema, we give examples of its use in representing complex materials systems. We also describe open-source tools that have been developed for building and reading files using the PIF schema.

Keywords

alloycompounddevicesmicrostructurepolymer
Type
Research Article
Information
MRS Bulletin , Volume 41 , Issue 8: Microstructure Informatics in Process–Structure–Property Relations , August 2016 , pp. 617 - 623
Copyright
Copyright © Materials Research Society 2016 

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References

Tolle, K.M., Tansley, D.S.W., Hey, A.J.G., Proc. IEEE 99, 1334 (2011).CrossRefGoogle Scholar
Rajan, K., Annu. Rev. Mater. Res. 45, 153 (2015).CrossRefGoogle Scholar
Rajan, K., Mater. Today 8, 38 (2005).CrossRefGoogle Scholar
MathWorks MATLAB, http://www.mathworks.com/products/matlab.Google Scholar
The R Project for Statistical Computing, https://www.r-project.org.Google Scholar
Scikit-learn: Machine Learning in Python, http://www.scikit-learn.org.Google Scholar
Apache Spark, http://spark.apache.org.Google Scholar
Weka, http://www.cs.waikato.ac.nz/ml/weka.Google Scholar
Frantzen, A., Sanders, D., Scheidtmann, J., Simon, U., Maier, W.F., QSAR Comb. Sci. 24, 22 (2005).CrossRefGoogle Scholar
Xu, Y., Yamazaki, M., Villars, P., Jpn. J. Appl. Phys. 50, 11RH02 (2011).CrossRefGoogle Scholar
“Materials Genome Initiative National Science and Technology Council Committee on Technology Subcommittee on the Materials Genome Initiative” (National Science and Technology Council Committee on Technology Subcommittee on the Materials Genome Initiative, Washington, DC, 2014).Google Scholar
Jain, A., Ong, S.P., Hautier, G., Chen, W., Richards, W.D., Dacek, S., Cholia, S., Gunter, D., Ceder, G., Persson, K.A., APL Mater. 1, 011002 (2013).CrossRefGoogle Scholar
Curtarolo, S., Setyawan, W., Wang, S., Xue, J., Yang, K., Taylor, R.H., Nelson, L.J., Hart, G.L.W., Sanvito, S., Buongiorno-Nardelli, M., Mingo, N., Levy, O., Comput. Mater. Sci. 58, 227 (2012).CrossRefGoogle Scholar
Saal, J.E., Kirklin, S., Aykol, M., Meredig, B., Wolverton, C., JOM 65, 1501 (2013).CrossRefGoogle Scholar
Hall, S.R., McMahon, B., Eds., International Tables for Crystallography Volume G: Definition and Exchange of Crystallographic Data (Springer, Dordrecht, 2005).Google Scholar
Warren, J.A., Boisvert, R.F., Building the Materials Innovation Infrastructure: Data and Standards (NISTIR 7898, National Institute of Standards and Technology, 2012).Google Scholar
Ward, C.H., Warren, J.A., Materials Genome Initiative: Materials Data (NISTIR 8038, National Institute of Standards and Technology, 2015).Google Scholar
National Institute of Standards and Technology, “NIST Materials Data Curation System,” https://mgi.nist.gov/materials-data-curation-system.Google Scholar
Huck, P., Jain, A., Gunter, D., Winston, D., Persson, K., Comput. Sci. Softw. Eng. (2015), available at http://arxiv.org/abs/1510.05024.Google Scholar
PIF Schema, http://www.citrine.io/pif.Google Scholar
Gaultois, M.W., Sparks, T.D., Borg, C.K.H., Seshadri, R., Bonificio, W.D., Clarke, D.R., Chem. Mater. 25, 2911 (2013).CrossRefGoogle Scholar
Zhang, S., Sun, N., He, X., Lu, X., Zhang, X., J. Phys. Chem. Ref. Data 35, 1475 (2006).CrossRefGoogle Scholar
Pypif, http://www.citrine.io/pypif.Google Scholar
Jpif, http://www.citrine.io/jpif.Google Scholar
Github, https://www.github.com.Google Scholar
Citrination, https://www.citrination.com.Google Scholar
Pandas, http://pandas.pydata.org.Google Scholar
Citrine Informatics, http://www.citrine.io.Google Scholar
https://commons.wikimedia.org/wiki/File:Elmer-pump-heatequation.png.Google Scholar
https://commons.wikimedia.org/wiki/File:BrittleAluminium320MPa_S-N_Curve.svg.Google Scholar
https://commons.wikimedia.org/wiki/File:Microstructure_of_rolled_and_annealed_brass;_magnification_400X.jpg.Google Scholar
https://commons.wikimedia.org/wiki/File:Grgr3d_small.gif.Google Scholar
https://commons.wikimedia.org/wiki/File:Atomic_resolution_Au100.JPG.Google Scholar
https://commons.wikimedia.org/wiki/File:Chalcopyrite-unit-cell-3D-balls.png.Google Scholar