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Provenance, workflows, and crystallographic tools in materials science: AiiDA, spglib, and seekpath

Published online by Cambridge University Press:  10 September 2018

Giovanni Pizzi ,
Atsushi Togo  and
Boris Kozinsky
Giovanni Pizzi
Affiliation:
École Polytechnique Fédérale de Lausanne, Switzerland; giovanni.pizzi@epfl.ch
Atsushi Togo
Affiliation:
Department of Materials Science and Engineering, Kyoto University, Japan; togo.atsushi@gmail.com
Boris Kozinsky
Affiliation:
Harvard University, USA; bkoz@seas.harvard.edu
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Abstract

The near-exponential expansion in computing resources over the last few decades has enabled a rapid increase in the capabilities of computational science, including applications to materials research. In order to harness the available resources and accelerate the field of materials design, it is critically important to develop robust and reusable automation software for preparing and performing multistep computational workflows, starting with crystal structures and ending with material properties. In the domain of first-principles calculations of crystalline materials, we highlight emerging tools for automated symmetry analysis of the atomic and electronic structure. With automation capabilities in hand, the ever-increasing amount of data also becomes a serious bottleneck in terms of organization, analysis, and reproducibility. We describe some of the progress and strategic challenges in the development of a general infrastructure for coupling computational automation with data management, emphasizing data reproducibility and provenance capture.

Keywords

simulationelectronic structurecrystallographic structurenanoscalenanostructure
Type
Data-Centric Science for Materials Innovation
Information
MRS Bulletin , Volume 43 , Issue 9: Data-Centric Science for Materials Innovation , September 2018 , pp. 696 - 702
Copyright
Copyright © Materials Research Society 2018 

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References

Samsonidze, G., Kozinsky, B., Adv. Energy Mater. 8 (20), 1800246R1 (2018).Google Scholar
Curtarolo, S., Setyawan, W., Hart, G.L.W., Jahnatek, M., Chepulskii, R.V., Taylor, R.H., Wang, S., Xue, J., Yang, K., Levy, O., Mehl, M.J., Stokes, H.T., Demchenko, D.O., Morgan, D., Comput. Mater. Sci. 58, 218 (2012).CrossRefGoogle Scholar
Jain, A., Ong, S.P., Hautier, G., Chen, W., Richards, W.D., Dacek, S., Cholia, S., Gunter, D., Skinner, D., Ceder, G., Persson, K.A., APL Mater. 1, 011002 (2013).CrossRefGoogle Scholar
Saal, J.E., Kirklin, S., Aykol, M., Meredig, B., Wolverton, C., JOM 65, 1501 (2013).CrossRefGoogle Scholar
Larsen, A.H., Mortensen, J.J., Blomqvist, J., Castelli, I.E., Christensen, R., Dulak, M., Friis, J., Groves, M.N., Hammer, B., Hargus, C., Hermes, E.D., Jennings, P.C., Jensen, P.B., Kermode, J., Kitchin, J.R., Kolsbjerg, E.L., Kubal, J., Kaasbjerg, K., Lysgaard, S., Maronsson, J.B., Maxson, T., Olsen, T., Pastewka, L., Peterson, A., Rostgaard, C., Schiøtz, J., Schütt, O., Strange, M., Thygesen, K.S., Vegge, T., Vilhelmsen, L., Walter, M., Zeng, Z., Jacobsen, K.W., J. Phys. Condens. Matter 29, 273002 (2017).CrossRefGoogle Scholar
Ong, S.P., Richards, W.D., Jain, A., Hautier, G., Kocher, M., Cholia, S., Gunter, D., Chevrier, V.L., Persson, K.A., Ceder, G., Comput. Mater. Sci. 68, 314 (2013).CrossRefGoogle Scholar
Pizzi, G., Cepellotti, A., Sabatini, R., Marzari, N., Kozinsky, B., Comput. Mater. Sci. 111, 218 (2016).CrossRefGoogle Scholar
AiiDA, http://www.aiida.net.Google Scholar
spglib, https://atztogo.github.io/spglib.Google Scholar
Hinuma, Y., Pizzi, G., Kumagai, Y., Oba, F., Tanaka, I., Comput. Mater. Sci. 128, 140 (2017).CrossRefGoogle Scholar
seekpath Web Interface, http://www.materialscloud.org/tools/seekpath.Google Scholar
Gražulis, S., Merkys, A., Vaitkus, A., Le Bail, A., Chateigner, D., Vilčiauskas, L., Cottenier, S., Björkman, T., Murray-Rust, P., Acta Crystallogr. A Found. Adv. 70, C1736 (2014).CrossRefGoogle Scholar
Nomad Meta-Info, https://metainfo.nomad-coe.eu/nomadmetainfopublic/archive.html.Google Scholar
Open Databases Integration for Materials Design, http://www.optimade.org.Google Scholar
Merkys, A., Mounet, N., Cepellotti, A., Marzari, N., Gražulis, S., Pizzi, G., J. Cheminform. 9, 56 (2017).CrossRefGoogle Scholar
Giannozzi, P., Andreussi, O., Brumme, T., Bunau, O., Nardelli, M.B., Calandra, M., Car, R., Cavazzoni, C., Ceresoli, D., Cococcioni, M., Colonna, N., Carnimeo, I., Dal Corso, A., de Gironcoli, S., Delugas, P., DiStasio, R.A. Jr., Ferretti, A., Floris, A., Fratesi, G., Fugallo, G., Gebauer, R., Gerstmann, U., Giustino, F., Gorni, T., Jia, J., Kawamura, M., Ko, H.-Y., Kokalj, A., Küçükbenli, E., Lazzeri, M., Marsili, M., Marzari, N., Mauri, F., Nguyen, N.L., Nguyen, H.-V., Otero-de-la-Roza, A., Paulatto, L., Poncé, S., Rocca, D., Sabatini, R., Santra, B., Schlipf, M., Seitsonen, A.P., Smogunov, A., Timrov, I., Thonhauser, T., Umari, P., Vast, N., Wu, X., Baroni, S., J. Phys. Condens. Matter 29, 465901 (2017).CrossRefGoogle Scholar
TCOD Database, http://www.crystallography.net/tcod.Google Scholar
Materials Cloud, https://www.materialscloud.org.Google Scholar
Mounet, N., Gibertini, M., Schwaller, Ph., Campi, D., Merkys, A., Marrazzo, A., Sohier, Th., Castelli, I.E., Cepellotti, A., Pizzi, G., Marzari, N., Nat. Nanotechnol. 13, 246 (2018).CrossRefGoogle Scholar
Mounet, N., Gibertini, M., Schwaller, Ph., Campi, D., Merkys, A., Marrazzo, A., Sohier, Th., Castelli, I.E., Cepellotti, A., Pizzi, G., Marzari, N., Materials Cloud Archive (2017), https://dx.doi.org/10.24435/materialscloud:2017.0008/v1.Google Scholar
Aroyo, M.I., Ed., International Tables for Crystallography A Space-Group Symmetry, 6th ed. (Wiley, 2016).CrossRefGoogle Scholar
Togo, A., Tanaka, I., Phys. Rev. B Condens. Matter 87, 184104 (2013).CrossRefGoogle Scholar
Grosse-Kunstleve, R.W., Adams, P.D., Acta Crystallogr. A 55, 383 (1999).CrossRefGoogle Scholar
Grosse-Kunstleve, R.W., Adams, P.D., Acta Crystallogr. A 58, 60 (2002).CrossRefGoogle Scholar
Hall, S.R., Acta Crystallogr. A 37, 517 (1981).CrossRefGoogle Scholar
Shmueli, U., Ed., International Tables for Crystallography B Reciprocal Space, 3rd ed. (Springer, Dordrecht, The Netherlands, 2008).Google Scholar
Hinuma, Y., Togo, A., Hayashi, H., Tanaka, I., Condens. Matter Mater. Sci. (2015), https://arxiv.org/abs/1506.01455.Google Scholar
Parthé, E., Gelato, L.M., Acta Crystallogr. A 40, 169 (1984).CrossRefGoogle Scholar
Grosse-Kunstleve, R.W., Sauter, N.K., Adams, P.D., Acta Crystallogr. A 60, 1 (2004).CrossRefGoogle Scholar
Bilbao Crystallographic Server, http://www.cryst.ehu.es.Google Scholar
Setyawan, W., Curtarolo, S., Comput. Mater. Sci. 49, 299 (2010).CrossRefGoogle Scholar
seekpath, http://github.com/giovannipizzi/seekpath.Google Scholar
AiiDA Plugins for Quantum ESPRESSO, http://github.com/aiidateam/aiida-quantumespresso.Google Scholar
Kozinsky, B., Akhade, S., Hirel, P., Hashibon, A., Elsässer, C., Mehta, P., Logeat, A., Eisele, U., Phys. Rev. Lett. 116, 055901 (2016).CrossRefGoogle Scholar
AiiDA Team, http://www.aiida.net/team.Google Scholar