The design of novel materials plays a key role in the advancement of technologies in any application field. It is therefore crucial that the materials research is pursued with optimal effectiveness and efficiency. Modern computational materials design in synergy with concepts from big data processing and -storage can largely contribute to meet this requirement. For example, the systematic investigation of a large set of bulk materials can be realized fast and cost effective with high-throughput (HT) electronic structure methods.
The general procedure for a HT-approach is to compute the properties of interest of a large set of possible materials. The information is then ideally stored in a searchable database. The last step is the materials search and selection. With statistical and graphical means, the properties of a large set of materials can be visualized. In fig. 1, the formation energy of a binary alloy (FePt) is shown as an example.
Fig. 1: The alloy formation energy of Fe-Pt alloys as function of the composition. Many different structures have been screened (red crosses) and the most stable structures lay on the blue line (convex hull). Data taken from the AFLOWLIB repository.
The data throughput in this approach is so high that no step of the data flow can rely on researcher's intervention. Evidently, this throughput is not easily reachable with experimental means.
The idea to have a searchable materials database is not new. There are several data-bases with experimentally obtained materials parameters. Researchers from all over the world have contributed experimental data to this data-bases for a very long time. Now For inorganic materials, there are for instance CRYSTMET1, ICSD2, Pearson’s Crystal Data3, Pauling File4, Inorganic Materials Database5, and binary alloy phase diagrams6,7. The computational data-bases can be seen as a complementation and extension of the experimental ones. Computationally, also rare, expensive or toxic compounds can be screened, finally leading to more complete information repositories. The latter, in turn, facilitates the investigation of property correlations which can help to establish a better understanding and prediction of material properties. Experiments can then be designed on the base of the computationally accumulated knowledge, for instance by focusing on the most promising candidates. Vice versa, computations underlay the requirement to correctly reproduce experimental results and to aid the interpretation of experimental measurements. Examples of online electronic structure repositories are the AFLOW8,9 consortium, the Materials Project10, the Computational Materials Repository11, the Electronic Structure Project12, Open Quantum Materials Database13, and the Carnegie Mellon’s Alloy Database14.
To contribute to the acceleration of materials discovery, the philosophy of high-throughput electronic structure methods is to create a “complete” extensive materials repository for the scientific community, preferably free-access and easy to use. Such a data-base should steadily updated and validated with experimental findings. The repositories can be searched (and extended) with the means of so-called descriptors and data-mining algorithms.
Author: Philomena Schlexer
1 P. S. White, J. R. Rodgers, Y .L. Page, Acta Cryst., B 58 (2002) 343
2 A. Belsky, M. Hellenbrandt, V. L. Karen, P. Luksch, Acta Cryst., B 58 (2002) 364
3 P. Villars, L. Calvert, Pearson’s Handbook of Crystallographic Data for Intermetallic Phases, 2nd ed., ASM International, Materials Park, OH, 1991
4 P. Villars, M. Berndt, K. Brandenburg, K. Cenzual, J. Daams, F. Hulliger, T. Massalski, H. Okamoto, K. Osaki, A. Prince, H. Putz, S. Iwata, J. All. Comp., (2004) 293
5 P. Villars, K. Cenzual, J.L.C. Daams, F. Hulliger, T.B. Massalski, H. Okamoto, K. Osaki, A. Prince, S. Iwata, Crystal Impact, Pauling File. Inorganic Materials Database and Design System, Binaries ed., ASM International, Metal Park, OH, 2003
6 T.B. Massalski, H. Okamoto, P.R. Subramanian, L. Kacprzak (Eds.), Binary Alloy Phase Diagrams, American Society for Metals, Materials Park, OH, 1990
7 P. Villars, M. Berndt, K. Brandenburg, K. Cenzual, J. Daams, F. Hulliger, T. Massalski, H. Okamoto, K. Osaki, A. Prince, H. Putz, S. Iwata, J. Alloys Comp., 367 (2004) 293
8 S. Curtarolo, W. Setyawan, G.L.W. Hart, M. Jahnatek, R. V. Chepulskii, R. H. Taylor, S. Wang, J. Xue, K. Yang, O. Levy, M. J. Mehl, H. T. Stokes, D. O. Demchenko, D. Morgan, Comp. Mat. Sci., 58 (2012) 218
9 S. Curtarolo, W. Setyawan, S. Wang, J. Xue, K. Yang, R. H. Taylor, L. J. Nelson, Comp. Mat. Sci., 58 (2012) 227
10 A. Jain, G. Hautier, C. J. Moore, S. P. Ong, C. C. Fischer, T. Mueller, K A. Persson, G. Ceder, Comp. Mat. Sci., 50 (2011) 2295
11 D. D. Landis, J. S. Hummelshoj, S. Nestorov, J. Greeley, M. Dulak, T. Bligaard, J. K. Norskov and K. W. Jacobsen, Comput. Sci. Eng., 14 (2012) 51
12 Klintenberg, M. The Electronic Structure Project; http://gurka.fysik.uu.se/esp-fs/
13 J. E. Saal, S. Kirklin, M. Aykol, B. Meredig, C. Wolverton, JOM, 65 (2013) 1501; http://www.oqmd.org/
14 M. Mihalkovič, M. Widom, Phys. Rev. B, 70 (2004) 144107