Definitions
To speed up the progress in the field of materials design, a number of challenges related to big data need to be addressed. This entry discusses these challenges and shows the semantic technologies that alleviate the problems related to variety, variability, and veracity.
Overview
Materials design and materials informatics are central for technological progress, not the least in the green engineering domain. Many traditional materials contain toxic or critical raw materials, whose use should be avoided or eliminated. Also, there is an urgent need to develop new environmentally friendly energy technology. Presently, relevant examples of materials design challenges include energy storage, solar cells, thermoelectrics, and magnetic transport (Ceder and Persson 2013; Jain et al. 2013; Curtarolo et al. 2013).
The space of potentially useful materials yet to be discovered – the so-called chemical white space– is immense. The possible combinations of, say, up to six different...
Keywords
- Material Ontology
- National Institute Of Materials Science (NIMS)
- Knowledge Representation Perspective
- Materials Genome Initiative
- Open Quantum Materials Database
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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References
Agrawal A, Alok C (2016) Perspective: materials informatics and big data: realization of the fourth paradigm of science in materials science. APL Mater 4:053,208:1–10. https://doi.org/10.1063/1.4946894
Ashino T (2010) Materials ontology: an infrastructure for exchanging materials information and knowledge. Data Sci J 9:54–61. https://doi.org/10.2481/dsj.008-041
Austin T (2016) Towards a digital infrastructure for engineering materials data. Mater Discov 3:1–12. https://doi.org/10.1016/j.md.2015.12.003
Belsky A, Hellenbrandt M, Karen VL, Luksch P (2002) New developments in the inorganic crystal structure database (ICSD): accessibility in support of materials research and design. Acta Crystallogr Sect B Struct Sci 58(3):364–369. https://doi.org/10.1107/S0108768102006948
Bergerhoff G, Hundt R, Sievers R, Brown ID (1983) The inorganic crystal structure data base. J Chem Inf Comput Sci 23(2):66–69. https://doi.org/10.1021/ci00038a003
Bernstein HJ, Bollinger JC, Brown ID, Grazulis S, Hester JR, McMahon B, Spadaccini N, Westbrook JD, Westrip SP (2016) Specification of the crystallographic information file format, version 2.0. J Appl Cryst 49:277–284. https://doi.org/10.1107/S1600576715021871
Bhat M, Shah S, Das P, Reddy S (2013) Premλp: knowledge driven design of materials and engineering process. In: ICoRD’13. Springer, pp 1315–1329. https://doi.org/10.1007/978-81-322-1050-4_105
Campbell CE, Kattner UR, Liu ZK (2014) File and data repositories for next generation CALPHAD. Scr Mater 70(Suppl C):7–11. https://doi.org/10.1016/j.scriptamat.2013.06.013
Ceder G, Persson KA (2013) The Stuff of Dreams. Sci Am 309:36–40
CEN (2010) A guide to the development and use of standards compliant data formats for engineering materials test data. European Committee for Standardization
Cheng X, Hu C, Li Y (2014) A semantic-driven knowledge representation model for the materials engineering application. Data Sci J 13:26–44. https://doi.org/10.2481/dsj.13-061/
Cheung K, Drennan J, Hunter J (2008) Towards an ontology for data-driven discovery of new materials. In: McGuinness D, Fox P, Brodaric B (eds) Semantic scientific knowledge integration AAAI/SSS workshop, pp 9–14
Curtarolo S, Setyawan W, Wang S, Xue J, Yang K, Taylor R, Nelson L, Hart G, Sanvito S, Buongiorno-Nardelli M, Mingo N, Levy O (2012) AFLOWLIB.ORG: a distributed materials properties repository from high-throughput ab initio calculations. Comput Mater Sci 58(Supplement C):227–235. https://doi.org/10.1016/j.commatsci.2012.02.002
Curtarolo S, Hart G, Buongiorno-Nardelli M, Mingo N, Sanvito S, Levy O (2013) The high-throughput highway to computational materials design. Nat Mater 12(3):191. https://doi.org/10.1038/nmat3568
Euzenat J, Shvaiko P (2007) Ontology matching. Springer, Berlin/Heidelberg
Faber F, Lindmaa A, von Lilienfeld A, Armiento R (2016) Machine learning energies of 2 million Elpasolite $(AB{C}_{2}{D}_{6})$ crystals. Phys Rev Lett 117(13):135,502. https://doi.org/10.1103/PhysRevLett.117.135502
Frenkel M, Chiroco RD, Diky V, Dong Q, Marsh KN, Dymond JH, Wakeham WA, Stein SE, Knigsberger E, Goodwin ARH (2006) XML-based IUPAC standard for experimental, predicted, and critically evaluated thermodynamic property data storage and capture (ThermoML) (IUPAC Recommendations 2006). Pure Appl Chem 78:541–612. https://doi.org/10.1351/pac200678030541
Frenkel M, Chirico RD, Diky V, Brown PL, Dymond JH, Goldberg RN, Goodwin ARH, Heerklotz H, Knigsberger E, Ladbury JE, Marsh KN, Remeta DP, Stein SE, Wakeham WA, Williams PA (2011) Extension of ThermoML: the IUPAC standard for thermodynamic data communications (IUPAC recommendations 2011). Pure Appl Chem 83:1937–1969. https://doi.org/10.1351/PAC-REC-11-05-01
Gangemi A, Guarino N, Masolo C, Oltramari A, Schneider L (2002) Sweetening ontologies with dolce. Knowledge engineering and knowledge management: ontologies and the semantic web, pp 223–233. https://doi.org/10.1007/3-540-45810-7_18
Gaultois MW, Oliynyk AO, Mar A, Sparks TD, Mulholland GJ, Meredig B (2016) Perspective: web-based machine learning models for real-time screening of thermoelectric materials properties. APL Mater 4(5):053,213. https://doi.org/10.1063/1.4952607
Ghiringhelli LM, Carbogno C, Levchenko S, Mohamed F, Huhs G, Lueders M, Oliveira M, Scheffler M (2016) Towards a common format for computational materials science data. PSI-K Scientific Highlights July
Glasser L (2016) Crystallographic information resources. J Chem Edu 93(3):542–549. https://doi.org/10.1021/acs.jchemed.5b00253
Grazulis S, Dazkevic A, Merkys A, Chateigner D, Lutterotti L, Quiros M, Serebryanaya NR, Moeck P, Downs RT, Le Bail A (2012) Crystallography open database (COD): an open-access collection of crystal structures and platform for world-wide collaboration. Nucleic Acids Res 40(Database issue):D420–D427. https://doi.org/10.1093/nar/gkr900
Hepp M (2008) Goodrelations: an ontology for describing products and services offers on the web. Knowl Eng Pract Patterns 329–346. https://doi.org/10.1007/978-3-540-87696-0_29
Ivanova V, Lambrix P (2013) A unified approach for debugging is-a structure and mappings in networked taxonomies. J Biomed Semant 4:10:1–10:19. https://doi.org/10.1186/2041-1480-4-10
Jain A, Ong SP, Hautier G, Chen W, Richards WD, Dacek S, Cholia S, Gunter D, Skinner D, Ceder G, Persson KA (2013) Commentary: the materials project: a materials genome approach to accelerating materials innovation. APL Mater 1(1):011,002. https://doi.org/10.1063/1.4812323
Kaufman JG, Begley EF (2003) MatML: a data interchange markup language. Adv Mater Process 161:35–36
Lambrix P, Strömbäck L, Tan H (2009) Information integration in bioinformatics with ontologies and standards. In: Bry F, Maluszynski J (eds) Semantic techniques for the Web, pp 343–376. https://doi.org/10.1007/978-3-642-04581-3_8
Larsen AH, Mortensen JJ, Blomqvist J, Castelli IE, Christensen R, Duak M, Friis J, Groves MN, Hammer B, Hargus C, Hermes ED, Jennings PC, Jensen PB, Kermode J, Kitchin JR, Kolsbjerg EL, Kubal J, Kaasbjerg K, Lysgaard S, Maronsson JB, Maxson T, Olsen T, Pastewka L, Peterson A, Rostgaard C, Schitz J, Schtt O, Strange M, Thygesen KS, Vegge T, Vilhelmsen L, Walter M, Zeng Z, Jacobsen KW (2017) The atomic simulation environment – a Python library for working with atoms. J Phys Condens Matter 29(27):273,002. https://doi.org/10.1088/1361-648X/aa680e
Lejaeghere K, Bihlmayer G, Bjrkman T, Blaha P, Blgel S, Blum V, Caliste D, Castelli IE, Clark SJ, Corso AD, Gironcoli Sd, Deutsch T, Dewhurst JK, Marco ID, Draxl C, Duak M, Eriksson O, Flores-Livas JA, Garrity KF, Genovese L, Giannozzi P, Giantomassi M, Goedecker S, Gonze X, Grns O, Gross EKU, Gulans A, Gygi F, Hamann DR, Hasnip PJ, Holzwarth NaW, Iuan D, Jochym DB, Jollet F, Jones D, Kresse G, Koepernik K, Kkbenli E, Kvashnin YO, Locht ILM, Lubeck S, Marsman M, Marzari N, Nitzsche U, Nordstrm L, Ozaki T, Paulatto L, Pickard CJ, Poelmans W, Probert MIJ, Refson K, Richter M, Rignanese GM, Saha S, Scheffler M, Schlipf M, Schwarz K, Sharma S, Tavazza F, Thunstrm P, Tkatchenko A, Torrent M, Vanderbilt D, van Setten MJ, Speybroeck VV, Wills JM, Yates JR, Zhang GX, Cottenier S (2016) Reproducibility in density functional theory calculations of solids. Science 351(6280):aad3000. https://doi.org/10.1126/science.aad3000
Moruzzi VL, Janak JF, Williams ARAR (2013) Calculated electronic properties of metals. Pergamon Press, New York
Mulholland GJ, Paradiso SP (2016) Perspective: materials informatics across the product lifecycle: selection, manufacturing, and certification. APL Mater 4(5):053,207. https://doi.org/10.1063/1.4945422
Murray-Rust P, Rzepa HS (2011) CML: evolution and design. J Cheminf 3:44. https://doi.org/10.1186/1758-2946-3-44
Murray-Rust P, Townsend JA, Adams SE, Phadungsukanan W, Thomas J (2011) The semantics of chemical markup language (CML): dictionaries and conventions. J Cheminfor 3:43. https://doi.org/10.1186/1758-2946-3-43
Pizzi G, Cepellotti A, Sabatini R, Marzari N, Kozinsky B (2016) AiiDA: automated interactive infrastructure and database for computational science. Comput Mater Sci 111(Supplement C):218–230. https://doi.org/10.1016/j.commatsci.2015.09.013
Premkumar V, Krishnamurty S, Wileden JC, Grosse IR (2014) A semantic knowledge management system for laminated composites. Adv Eng Inf 28(1):91–101. https://doi.org/10.1016/j.aei.2013.12.004
Radinger A, Rodriguez-Castro B, Stolz A, Hepp M (2013) Baudataweb: the Austrian building and construction materials market as linked data. In: Proceedings of the 9th international conference on semantic systems. ACM, pp 25–32. https://doi.org/10.1145/2506182.2506186
Rajan K (2015) Materials informatics: the materials Gene and big data. Annu Rev Mater Res 45:153–169. https://doi.org/10.1146/annurev-matsci-070214-021132
Saal JE, Kirklin S, Aykol M, Meredig B, Wolverton C (2013) Materials design and discovery with high-throughput density functional theory: the open quantum materials database (OQMD). JOM 65(11):1501–1509. https://doi.org/10.1007/s11837-013-0755-4
Soldatova LN, King RD (2006) An ontology of scientific experiments. J R Soc Interface 3(11):795–803. https://doi.org/10.1098/rsif.2006.0134
Swindells N (2009) The representation and exchange of material and other engineering properties. Data Sci J 8:190–200. https://doi.org/10.2481/dsj.008-007
van der Vet P, Speel PH, Mars N (1994) The Plinius ontology of ceramic materials. In: Mars N (ed) Workshop notes ECAI’94 workshop comparison of implemented ontologies, pp 187–205
Vardeman C, Krisnadhi A, Cheatham M, Janowicz K, Ferguson H, Hitzler P, Buccellato A (2017) An ontology design pattern and its use case for modeling material transformation. Semant Web 8:719–731. https://doi.org/10.3233/SW-160231
Zhang X, Hu C, Li H (2009) Semantic query on materials data based on mapping matml to an owl ontology. Data Sci J 8:1–17. https://doi.org/10.2481/dsj.8.1
Zhang X, Zhao C, Wang X (2015a) A survey on knowledge representation in materials science and engineering: an ontological perspective. Comput Ind 73:8–22. https://doi.org/10.1016/j.compind.2015.07.005
Zhang Y, Luo X, Zhao Y, chao Zhang H (2015b) An ontology-based knowledge framework for engineering material selection. Adv Eng Inf 29:985–1000. https://doi.org/10.1016/j.aei.2015.09.002
Zhang X, Pan D, Zhao C, Li K (2016) MMOY: towards deriving a metallic materials ontology from Yago. Adv Eng Inf 30:687–702. https://doi.org/10.1016/j.aei.2016.09.002
Zhang X, Chen H, Ruan Y, Pan D, Zhao C (2017) MATVIZ: a semantic query and visualization approach for metallic materials data. Int J Web Inf Syst 13:260–280. https://doi.org/10.1108/IJWIS-11-2016-0065
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Lambrix, P., Armiento, R., Delin, A., Li, H. (2018). Big Semantic Data Processing in the Materials Design Domain. In: Sakr, S., Zomaya, A. (eds) Encyclopedia of Big Data Technologies. Springer, Cham. https://doi.org/10.1007/978-3-319-63962-8_293-1
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DOI: https://doi.org/10.1007/978-3-319-63962-8_293-1
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Big Semantic Data Processing in the Materials Design Domain- Published:
- 22 March 2018
DOI: https://doi.org/10.1007/978-3-319-63962-8_293-1
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FAIR Big Data in the Materials Design Domain- Published:
- 24 February 2012
DOI: https://doi.org/10.1007/978-3-319-63962-8_293-2