journal article·2018-08-20Open access

Online search tool for graphical patterns in electronic band structures

Stanislav S. Borysov, Bart Olsthoorn, M. Berk Gedik, R. Matthias Geilhufe, Alexander V. Balatsky

npj Computational Materials· Volume 4· Issue 1

Abstract

Abstract Many functional materials can be characterized by a specific pattern in their electronic band structure, for example, Dirac materials, characterized by a linear crossing of bands; topological insulators, characterized by a “Mexican hat” pattern or an effectively free electron gas, characterized by a parabolic dispersion. To find material realizations of these features, manual inspection of electronic band structures represents a relatively easy task for a small number of materials. However, the growing amount of data contained within modern electronic band structure databases makes this approach impracticable. To address this problem, we present an automatic graphical pattern search tool implemented for the electronic band structures contained within the Organic Materials Database. The tool is capable of finding user-specified graphical patterns in the collection of thousands of band structures from high-throughput calculations in the online regime. Using this tool, it only takes a few seconds to find an arbitrary graphical pattern within the ten electronic bands near the Fermi level for 26,739 organic crystals. The source code of the developed tool is freely available and can be adapted to any other electronic band structure database.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Machine Learning in Materials Science · Topological Materials and Phenomena · Topological and Geometric Data Analysis

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

References (55)

Show source-provided references
1. Rodgers, J. R. & Cebon, D. Materials informatics. Mrs. Bull. 31, 975–980 (2006).10.1557/mrs2006.223 ↗
2. Ferris, K. F., Peurrung, L. M. & Marder, J. M. Materials informatics: fast track to new materials. Adv. Mater. Process. 165, 50–51 (2007).
3. Curtarolo, S. et al. The high-throughput highway to computational materials design. Nat. Mater. 12, 191–201 (2013).10.1038/nmat3568 ↗
4. Wang, S., Wang, Z., Setyawan, W., Mingo, N. & Curtarolo, S. Assessing the thermoelectric properties of sintered compounds via high-throughput ab-initio calculations. Phys. Rev. X 1, 021012 (2011).
5. Greeley, J., Jaramillo, T. F., Bonde, J., Chorkendorff, I. & Norskov, J. K. Computational high-throughput screening of electrocatalytic materials for hydrogen evolution. Nat. Mater. 5, 909–913 (2006).10.1038/nmat1752 ↗
6. Hautier, G. et al. Phosphates as lithium-ion battery cathodes: an evaluation based on high-throughput ab initio calculations. Chem. Mater. 23, 3495–3508 (2011).10.1021/cm200949v ↗
7. Klintenberg, M., Haraldsen, J. T. & Balatsky, A. V. Computational search for strong topological insulators: an exercise in data mining and electronic structure. Appl. Phys. Res. 6, 31 (2014).10.5539/apr.v6n4p31 ↗
8. Geilhufe, R. M., Borysov, S. S., Bouhon, A. & Balatsky, A. V. Data mining for three-dimensional organic Dirac materials: focus on space group 19. Sci. Rep. 7, 7298 (2017).10.1038/s41598-017-07374-7 ↗
9. Geilhufe, R. M., Bouhon, A., Borysov, S. S. & Balatsky, A. V. Three-dimensional organic Dirac-line materials due to nonsymmorphic symmetry: a data mining approach. Phys. Rev. B 95, 041103 (2017).10.1103/PhysRevB.95.041103 ↗
10. Rasmussen, F. A. & Thygesen, K. S. Computational 2D materials database: electronic structure of transition-metal dichalcogenides and oxides. J. Phys. Chem. C. 119, 13169–13183 (2015).10.1021/acs.jpcc.5b02950 ↗
11. Jain, A. et al. The materials project: a materials genome approach to accelerating materials innovation. APL Mater. 1, 011002 (2013).10.1063/1.4812323 ↗
12. Borysov, S. S., Geilhufe, R. M. & Balatsky, A. V. Organic materials database: an open-access online database for data mining. PLoS ONE 12, e0171501 (2017).10.1371/journal.pone.0171501 ↗
13. Bradlyn, B. et al. Beyond Dirac and Weyl fermions: unconventional quasiparticles in conventional crystals. Science 353, aaf5037 (2016).10.1126/science.aaf5037 ↗
14. Bradlyn, B. et al. Topological quantum chemistry. Nature 547, 298–305 (2017).10.1038/nature23268 ↗
15. Wieder, B. J. & Kane, C. L. Spin-orbit semimetals in the layer groups. Phys. Rev. B 94, 155108 (2016).10.1103/PhysRevB.94.155108 ↗
16. Bouhon, A. & Black-Schaffer, A. M. Global band topology of simple and double Dirac-point semimetals. Phys. Rev. B 95, 241101 (2017).10.1103/PhysRevB.95.241101 ↗
17. Bzdušek, T., Wu, Q., Rüegg, A., Sigrist, M. & Soluyanov, A. A. Nodal-chain metals. Nature 538, 75 (2016).10.1038/nature19099 ↗
18. Novoselov, K. S. et al. Two-dimensional gas of massless Dirac fermions in graphene. Nature 438, 197–200 (2005).10.1038/nature04233 ↗
19. Xu, S.-Y. et al. Discovery of a Weyl fermion semimetal and topological Fermi arcs. Science 349, 613–617 (2015).10.1126/science.aaa9297 ↗
20. Soluyanov, A. A. et al. Type-II Weyl semimetals. Nature 527, 495–498 (2015).10.1038/nature15768 ↗
21. Volovik, G. E. & Zhang, K. Lifshitz transitions, type-II Dirac and Weyl fermions, event horizon and all that. J. Low. Temp. Phys. 189, 276–299 (2017).10.1007/s10909-017-1817-8 ↗
22. Wieder, B. J., Kim, Y., Rappe, A. M. & Kane, C. L. Double Dirac semimetals in three dimensions. Phys. Rev. Lett. 116, 186402 (2016).10.1103/PhysRevLett.116.186402 ↗
23. Yu, R., Weng, H., Fang, Z., Dai, X. & Hu, X. Topological node-line semimetal and Dirac semimetal state in antiperovskite Cu 3 PdN. Phys. Rev. Lett. 115, 036807 (2015).10.1103/PhysRevLett.115.036807 ↗
24. Wang, Z., Alexandradinata, Aris., Cava, R. J. & Bernevig, B. A. Hourglass fermions. Nature 532, 189–194 (2016).10.1038/nature17410 ↗
25. Lv, B. et al. Observation of three-component fermions in the topological semimetal molybdenum phosphide. Nature 546, 627–631 (2017).10.1038/nature22390 ↗
26. Setyawan, W. & Curtarolo, S. High-throughput electronic band structure calculations: challenges and tools. Comput. Mater. Sci. 49, 299–312 (2010).10.1016/j.commatsci.2010.05.010 ↗
27. ANNOY library. https://github.com/spotify/annoy, accessed 01 Aug 2017.
28. Sarmiento-Perez, R., Cerqueira, T. F. T., Körbel, S., Botti, S. & Marques, M. A. L. Prediction of stable nitride perovskites. Chem. Mater. 27, 5957–5963 (2015).10.1021/acs.chemmater.5b02026 ↗
29. Ortiz, C., Eriksson, O. & Klintenberg, M. Data mining and accelerated electronic structure theory as a tool in the search for new functional materials. Comput. Mater. Sci. 44, 1042–1049 (2009).10.1016/j.commatsci.2008.07.016 ↗
30. Yan, Q., Chen, R. & Neaton, J. Data-driven discovery of new Dirac semimetal materials. Bull. Am. Phys. Soc. 62 (2017). BAPS.2017.MAR.H1.5, http://meetings.aps.org/link/BAPS.2017.MAR.H1.5.
31. Klintenberg, M. & Eriksson, O. Possible high-temperature superconductors predicted from electronic structure and data-filtering algorithms. Comput. Mater. Sci. 67, 282–286 (2013).10.1016/j.commatsci.2012.08.038 ↗
32. Geilhufe, R. M., Borysov, S. S., Kalpakchi, D., & Balatsky, A. V. Towards novel organic high-Tc superconductors: data mining using density of states similarity search. Phys. Rev. Mater. 2, 024802 (2018).10.1103/PhysRevMaterials.2.024802 ↗
33. Herper, H. C. et al. Combining electronic structure and many-body theory with large databases: a method for predicting the nature of 4f states in Ce compounds. Phys. Rev. Mater. 1, 033802 (2017).10.1103/PhysRevMaterials.1.033802 ↗
34. Sarma, S. D., Adam, S., Hwang, E. H. & Rossi, E. Electronic transport in two-dimensional graphene. Rev. Mod. Phys. 83, 407–470 (2011).10.1103/RevModPhys.83.407 ↗
35. Abergel, D. S. L., Apalkov, V., Berashevich, J., Ziegler, K. & Chakraborty, T. Properties of graphene: a theoretical perspective. Adv. Phys. 59, 261–482 (2010).10.1080/00018732.2010.487978 ↗
36. Hasan, M. Z. & Kane, C. L. Colloquium: topological insulators. Rev. Mod. Phys. 82, 3045 (2010).10.1103/RevModPhys.82.3045 ↗
37. Tanaka, Y. et al. Experimental realization of a topological crystalline insulator in SnTe. Nat. Phys. 8, 800–803 (2012).10.1038/nphys2442 ↗
38. Geilhufe, M. et al. Effect of hydrostatic pressure and uniaxial strain on the electronic structure of Pb1−xSnxTe. Phys. Rev. B 92, 235203 (2015).10.1103/PhysRevB.92.235203 ↗
39. Hsieh, T. H. et al. Topological crystalline insulators in the SnTe material class. Nat. Commun. 3, 982 (2012).10.1038/ncomms1969 ↗
40. Chen, Y. L. et al. Experimental realization of a three-dimensional topological insulator, Bi2Te3. Science 325, 178–181 (2009).10.1126/science.1173034 ↗
41. Hohenberg, P. & Kohn, W. Inhomogeneous electron gas. Phys. Rev. 136, B864–B871 (1964).10.1103/PhysRev.136.B864 ↗
42. Kohn, W. & Sham, L. J. Self-consistent equations including exchange and correlation effects. Phys. Rev. 140, A1133–A1138 (1965).10.1103/PhysRev.140.A1133 ↗
43. Gražulis, S. et al. Crystallography open database—an open-access collection of crystal structures. J. Appl. Crystallogr. 42, 726–729 (2009).10.1107/S0021889809016690 ↗
44. Kresse, G. & Furthmüller, J. Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set. Comput. Mater. Sci. 6, 15–50 (1996).10.1016/0927-0256(96)00008-0 ↗
45. Ong, S. P. et al. Python materials genomics (pymatgen): a robust, open-source python library for materials analysis. Comput. Mater. Sci. 68, 314–319 (2013).10.1016/j.commatsci.2012.10.028 ↗
46. Agrawal, R, Lin, K.-I, Sawhney, H. S., & Shim, K. Fast similarity search in the presence of noise, scaling, and translation in time-series databases. (eds Dayal, U., Gray, P. M. D. & N., Shojiro) In Proceedings of the 21st International Conference on Very Large Data Bases, VLDB ’95, 490–501 (Morgan Kaufmann Publishers Inc, San Francisco, CA, USA, 1995).
47. Agrawal, R., Faloutsos, C. & Swami, A. Efficient similarity search in sequence databases. In Foundations of Data Organization and Algorithms (ed Lomet, D. B.) 69–84 (Springer, Berlin, Heidelberg, 1993).10.1007/3-540-57301-1_5 ↗
48. Chan, K.-P. & Fu, A. W.-C. Efficient time series matching by wavelets. In Proceedings of the15th International Conference on Data Engineering (Cat. no.99CB36337) 126–133 (eds Kitsuregawa, M., Maciaszek, L., Papazoglou, M. & Pu C., IEEE Computer Society Press, Los Alamitos, CA, USA, 1999).10.1109/ICDE.1999.754915 ↗
49. Keogh, E., Chakrabarti, K., Pazzani, M. & Mehrotra, S. Dimensionality reduction for fast similarity search in large time series databases. Knowl. Inf. Syst. 3, 263–286 (2001).10.1007/PL00011669 ↗
50. Keogh, E. & Smyth, P. A probabilistic approach to fast pattern matching in time series databases. In Proceedings of the Third International Conference on Knowledge Discovery and Data Mining, KDD’97 (eds Heckerman, D., Mannila, H., Pregibon, D., Uthurusamy, R.), 24–30 (AAAI Press, Menlo Park, CA, USA, 1997).
51. Yianilos, P. Nin Data structures and algorithms for nearest neighbor search in general metric spaces. (ed Ramachandran, V.) In Proceedings of the Fourth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA’93, 311–321 (Society for Industrial and Applied Mathematics, Philadelphia, PA, USA, 1993).
52. Andoni, A. & Indyk, P. Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions. In 2006 47th Annual IEEE Symposium on Foundations of Computer Science (FOCS’06) (ed Arora, S.), 459–468 (IEEE Computer Society Press, Los Alamitos, CA, USA, 2006).10.1109/FOCS.2006.49 ↗
53. Johnson, J., Douze, M. & Jégou, H. Billion-scale similarity search with GPUs. arXiv preprint arXiv:1702.08734 (2017).
54. Boytsov, L. & Naidan, B. Engineering efficient and effective non-metric space library. In Similarity Search and Applications - 6th International Conference, SISAP 2013, A Coruña, Spain, October 2–4, 2013, Proceedings, 280–293 (eds Brisaboa, N., Pedreira, O., Zezula, P., Springer, Heidelberg, 2013).
55. Highsoft AS. http://highcharts.com, accessed 22 June 2018.