Journal of Computational Intelligence in Materials Science


Analyzing the Current Landscape of Materials Data Infrastructures



Journal of Computational Intelligence in Materials Science

Received On : 10 October 2023

Revised On : 20 January 2024

Accepted On : 16 February 2024

Published On : 24 February 2024

Volume 02, 2024

Pages : 036-046


Abstract


The development and production of engineering materials receive substantial investments from both the industrial and research sectors. The materials are produced and certified in adherence to a set of product and evaluation criteria that have progressed over numerous decades to fulfill increasingly rigorous specifications. However, the considerable amount of data generated from these endeavors remains predominantly inaccessible. The endeavor to create a digital framework for engineering materials data can be dated back to over thirty years ago. However, the extensive implementation of machine-readable formats that facilitate the regular exchange of engineering materials data is yet to be accomplished. This article provides a comprehensive overview of the development of materials data infrastructures that are designed to gather, store, and disseminate materials data to various stakeholders. Prior to delving into the present state of digital materials infrastructures, we shall first contemplate the early iterations of such infrastructures. Several challenges that must be addressed before the development of a robust materials search engines and discovery tool are also reviewed.


Keywords


Materials Infrastructures, Materials Discovery, Materials Science, Materials Data, Aalto Materials Digitalization Platform, Novel Materials Discovery.


  1. M. Rubacha, A. K. Rattan, and S. C. Hosselet, “A review of electronic laboratory notebooks available in the market today,” J. Lab. Autom., vol. 16, no. 1, pp. 90–98, 2011.
  2. P. J. Prasad and G. L. Bodhe, “Trends in laboratory information management system,” Chemometr. Intell. Lab. Syst., vol. 118, pp. 187–192, 2012.
  3. A. Jafari and A. Sadeghi, “A new insight into the mechanical properties of nanobiofibers and vibrational behavior of atomic force microscope beam considering them as the samples,” J. Mech. Behav. Biomed. Mater., vol. 142, p. 105842, 2023.
  4. M. Cordle et al., “Impact of radius and skew angle on areal density in heat assisted magnetic recording hard disk drives,” AIP Adv., vol. 8, no. 5, p. 056507, 2018.
  5. C. Pazzanese, “Clinton reflects on foreign policy triumphs and challenges,” Harvard Gazette, 08-Apr-2021. [Online]. Available: https://news.harvard.edu/gazette/story/2021/04/clinton-reflects-on-foreign-policy-triumphs-and-challenges/. [Accessed: 30-Apr-2023].
  6. T. Chen, “A tailored non-linear fluctuation smoothing rule for semiconductor manufacturing factory scheduling,” Proc Inst Mech Eng Part I J Syst Control Eng, vol. 223, no. 2, pp. 149–160, 2009.
  7. M. Drapa, “University of Chicago alum John B. Goodenough shares Nobel Prize for invention of lithium-ion battery,” University of Chicago, 09-Oct-2019. [Online]. Available: https://news.uchicago.edu/story/john-b-goodenough-shares-nobel-prize-invention-lithium-ion-battery. [Accessed: 30-Apr-2023].
  8. A. Antón, M. Torrellas, V. Raya, and J. I. Montero, “Modelling the amount of materials to improve inventory datasets of greenhouse infrastructures,” Int. J. Life Cycle Assess., vol. 19, no. 1, pp. 29–41, 2014.
  9. P. Michail and K. Christos, “Object relational mapping vs. Event-sourcing: Systematic review,” in Electronic Government and the Information Systems Perspective, Cham: Springer International Publishing, 2022, pp. 18–31.
  10. P. Tuli and J. P. Patra, “Symbol question conversion in structured query language using fuzzy with deep attention based rain LSTM,” Multimed. Tools Appl., vol. 81, no. 22, pp. 32323–32349, 2022.
  11. J. L. Christiansen, B. D. Clarke, C. J. Burke, J. M. Jenkins, and the Kepler Completeness Working Group, “The Kepler completeness study: A pipeline throughput experiment,” Proc. Int. Astron. Union, vol. 8, no. S293, pp. 88–93, 2012.
  12. J. Wang, M. Chao, G. Lin, C. Gao, and D. Liu, “Research on the construction of information service platforms for electricity market large data: Research on the construction of information service platforms for electricity market large data,” in Advances in Energy, Environment and Materials Science, CRC Press, 2016, pp. 9–13.

Acknowledgements


The author(s) received no financial support for the research, authorship, and/or publication of this article.


Funding


No funding was received to assist with the preparation of this manuscript.


Ethics declarations


Conflict of interest

The authors have no conflicts of interest to declare that are relevant to the content of this article.


Availability of data and materials


No data available for above study.


Author information


Contributions

All authors have equal contribution in the paper and all authors have read and agreed to the published version of the manuscript.


Corresponding author


Rights and permissions


Open Access This article is licensed under a Creative Commons Attribution NoDerivs is a more restrictive license. It allows you to redistribute the material commercially or non-commercially but the user cannot make any changes whatsoever to the original, i.e. no derivatives of the original work. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/4.0/


Cite this article


Easu Kwesiga Jonan, “Analyzing the Current Landscape of Materials Data Infrastructures”, Journal of Computational Intelligence in Materials Science, vol.2, pp. 036-046, 2024. doi: 10.53759/832X/JCIMS202402004.


Copyright


© 2024 Easu Kwesiga Jonan. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.