- 670 Downloads
Materials informatics employs techniques, tools, and theories drawn from the emerging fields of data science, internet, computer science and engineering, and digital technologies to the materials science and engineering to accelerate materials, products and manufacturing innovations. Manufacturing is transforming into shorter design cycles, mass customization, on-demand production, and sustainable products. Additive manufacturing or 3D printing is a popular example of such a trend. However, the success of this manufacturing transformation is critically dependent on the availability of suitable materials and of data on invertible processing–structure–property–performance life cycle linkages of materials. Experience suggests that the material development cycle, i.e. the time to develop and deploy new material, generally exceeds the product design and development cycle. Hence, there is a need to accelerate materials innovation in order to keep up with product and manufacturing innovations. This is a major challenge considering the hundreds of thousands of materials and processes, and the huge amount of data on microstructure, composition, properties, and functional, environmental, and economic performance of materials. Moreover, the data sharing culture among the materials community is sparse. Materials informatics is key to the necessary transformation in product design and manufacturing. Through the association of material and information sciences, the emerging field of materials informatics proposes to computationally mine and analyze large ensembles of experimental and modeling datasets efficiently and cost effectively and to deliver core materials knowledge in user-friendly ways to the designers of materials and products, and to the manufacturers. This paper reviews the various developments in materials informatics and how it facilitates materials innovation by way of specific examples.
KeywordsMaterials informatics Materials data analytics Materials modelling Materials data mining Materials selection Materials web platform Materials 4.0
Seeram Ramakrishna acknowledges support from Lloyds Register Foundation Grant LRF WBS 265-000-553-597. Surya R. Kalidini acknowledges support from NIST Grant 70NANB14H191. W.C. Lu, Q. Qian and T.Y. Zhang acknowledge support from National Key Research and Development Program of China (2016YFB0700504, and Science and Technology Commission of Shanghai Municipality (Nos. 15DZ2260300 and 16DZ2260600), China. Stefano Sanvito acknowledge support from Science Foundation of Ireland (14/IA/2624 and AMBER Center).
- Adams, B. L., Kalidindi, S. R., & Fullwood, D. (2012). Microstructure sensitive design for performance optimization. Oxford: Butterworth-Heinemann.Google Scholar
- Agrawal, A., Deshpande, P. D., Cecen, A., Gautham, B. P., Choudhary, A. N., & Kalidindi, S. R. (2014). Exploration of data science techniques to predict fatigue strength of steel from composition and processing parameters. Integrating Materials and Manufacturing Innovation, 3, 8. https://doi.org/10.1186/2193-9772-3-8.CrossRefGoogle Scholar
- Balachandran, P. V., Xue, D., Theiler, J., Hogden, J., & Lookman, T. (2016). Adaptive strategies for materials design using uncertainties. Scientific Reports, 6 (1966).Google Scholar
- Bergamaschi, E., Murphy, F., Poland, C. A., Mullins, M., Costa, A. L., Mcalea, E., et al. (2015). Impact and effectiveness of risk mitigation strategies on the insurability of nanomaterial production: Evidences from industrial case studies. Wiley Interdisciplinary Reviews: Nanomedicine and Nanobiotechnology, 7(6), 839–855. https://doi.org/10.1002/wnan.1340.Google Scholar
- Brownlee, J. (2013) A tour of machine learning algorithms. http://machinelearningmastery.com/a-tour-of-machine-learning-algorithms/.Accessed 2012.
- de Jong, M., Chen, W., Notestine, R., Persson, K., Ceder, G., Jain, A., et al. (2016). A statistical learning framework for materials science: Application to elastic moduli of k-nary inorganic polycrystalline compounds. Scientific Reports, 6, 34256. https://doi.org/10.1038/srep34256.CrossRefGoogle Scholar
- de Pablo, J. J., Jones, B., Kovacs, C. L., Ozolins, V., & Ramirez, A. P. (2014). The materials genome initiative, the interplay of experiment, theory and computation. Current Opinion in Solid State and Materials Science, 18(2), 99–117. https://doi.org/10.1016/j.cossms.2014.02.003.CrossRefGoogle Scholar
- Gen, M., & Cheng, R. (1997). Genetic algorithms and engineering design. New York: Wiley.Google Scholar
- Kalidindi, S. R. (2015). Hierarchical materials informatics. Oxford: Butterworth Heinemann.Google Scholar
- Kalidindi, S. R., Niezgoda, S. R., Landi, G., Vachhani, S., & Fast, T. (2010). A novel framework for building materials knowledge systems. Computers, Materials & Continua, 17, 103–125.Google Scholar
- Kalidindi, S. R., & De Graef, M. (2015). Materials data science: Current status and future outlook, annual review of materials research. Annual Reviews, 45(1), 171–193. https://doi.org/10.1146/annurev-matsci-070214-020844.Google Scholar
- Landi, G., & Kalidindi, S. R. (2010B). Thermo-elastic localization relationships for multi-phase composites. Computers, Materials & Continua, 16, 273–293.Google Scholar
- Low, J. S. C., Lu, W. F., & Song, B. (2014). Methodology for an integrated life cycle approach to design for environment. Key Engineering Materials,. https://doi.org/10.4028/www.scientific.net/KEM.572.20.Google Scholar
- Mitchell, T. M. (1997). Machine Learning. New York City: McGraw Hill.Google Scholar
- National Science and Technology Council. (2011). Materials genome initiative for global competitiveness. Washington: National Science and Technology Council.Google Scholar
- NISP. (2015a). A brief introduction to CRISP. National Industrial Symbiosis Programme. http://sdrn.policystudiesinstitute.org.uk/sites/default/files/events/Paul Innes CRI (Accessed October 25, 2015).
- NISP. (2015b). Confidentiality Charter CRISP. National Industrial Symbiosis Programme. Available at: https://www.tees.ac.uk/docs/DocRepo/Clemance/NISPConfidentialtyCharter.pdf (Accessed: 26 October 2015).
- NIST. (2013). Materials informatics. National Institute of Standards and Technology. https://www.nist.gov/programs-projects/materials-informatics. Accessed October 30, 2016.
- Quinlan, J. R. (1993). C4.5: Programs for machine learning. San Mateo, CA: Morgan Kaufmann.Google Scholar
- Raabe, B., Low, J. S. C., Juraschek, M., Herrmann, C., Tjandra, T. B., Ng, Y. T., et al. (2017). Collaboration platform for enabling industrial symbiosis? Application of the by-product exchange network model. Procedia CIRP, 61, 263–268. https://doi.org/10.1016/j.procir.2016.11.225.CrossRefGoogle Scholar
- Rajan, Jose, & Seeram, Ramakrishna, (2018). Materials 4.0: Materials big data enabled materials discovery. Applied Materials Today. https://doi.org/10.1016/j.apmt.2017.12.015
- Rajan, K. (2015). Materials informatics: The materials gene and big data. Annual Review of Materials Research, 45(1), 153–169. https://doi.org/10.1146/annurev-matsci-070214-021132.CrossRefGoogle Scholar
- Rodgers, J. R., & Cebon, D. (2006). Materials informatics. MRS Bulletin,31(12), 975–980. https://www.cambridge.org/core/article/materials-informatics/4DDA16B3B93C616EBAE618445488A09B.
- Shalev-Shwartz, S. (2011). Online learning and online convex optimization. Foundations and Trends in Machine Learning., 4(2), 107–194. https://doi.org/10.1561/2200000018.
- Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B (Methodological),58(1), 267–88. http://www.jstor.org/stable/2346178.
- Vapnik, V. (1998). Statistical learning theory. New York: Wiley.Google Scholar
- Zhang, H. C., Li, J., Shrivastava, P., Whitley, A., & Merchant, M. E. (2004). A web-based system for reverse manufacturing and product environmental impact assessment considering enf-of-life dispositions. CIRP Annals Manufacturing Technology, 53(1), 5–8. https://doi.org/10.1016/S0007-8506(07)60632-5.CrossRefGoogle Scholar