Journal of Computational Intelligence in Materials Science

A Review on Background and Applications of Machine Learning in Materials Research

Journal of Computational Intelligence in Materials Science

Received On : 15 March 2023

Revised On : 20 April 2023

Accepted On : 15 June 2023

Published On : 30 June 2023

Volume 01, 2023

Pages : 077-087


In recent decades, Artificial Intelligence (AI) has garnered considerable interest owing to its potential to facilitate greater levels of automation and speed up overall output. There has been a significant increase in the quantity of training data sets, processing capacity, and deep learning techniques that are all favorable to the widespread use of AI in fields like material science. Attempting to learn anything new by trial and error is a slow and ineffective approach. Therefore, AI, and particularly machine learning, may hasten the process by gleaning rules from information and constructing predictive models. In traditional computational chemistry, human scientists give the formulae, and the computer just crunches the numbers. In this article, we take a look back at the ways in which artificial intelligence has been put to use in the creation of new materials, such as in their design, performance prediction, and synthesis. In these programs, an emphasis is placed on the specifics of AI methodology implementation and the benefits gained over more traditional approaches. The last section elaborates, from both an algorithmic and an infrastructural perspective, where AI is headed in the future.


Machine Learning, Materials Research, Materials Engineering, K-Nearest Neighbor, Artificial Neural Networks.

  1. W. Sha et al., “Artificial intelligence to power the future of materials science and engineering,” Adv. Intell. Syst., vol. 2, no. 4, p. 1900143, 2020.
  2. B. G. Hyde and S. Andersson, Inorganic Crystal Structure. Nashville, TN: John Wiley & Sons, 1989.
  3. M. Pentz, Handling Experimental Data. Buckingham, England: Open University Press, 1988.
  4. J. X.-Y. Lim, D. Leow, Q.-C. Pham, and C.-H. Tan, “Development of a robotic system for automatic organic chemistry synthesis,” IEEE Trans. Autom. Sci. Eng., vol. 18, no. 4, pp. 2185–2190, 2021.
  5. P. Krishnamurthi, Y. Raju, Y. Khambhaty, and P. T. Manoharan, “Zinc Oxide-Supported Copper Clusters with High Biocidal Efficacy for Escherichia coli and Bacillus cereus,” ACS Omega, vol. 2, no. 6, pp. 2524–2535, 2017.
  6. S. Magnussen and D. Burgess, “Stochastic resampling techniques for quantifying error propagations in forest field experiments,” Can. J. For. Res., vol. 27, no. 5, pp. 630–637, 1997.
  7. J. Hulva et al., “Unraveling CO adsorption on model single-atom catalysts,” Science, vol. 371, no. 6527, pp. 375–379, 2021.
  8. Z. Jiang et al., “Ag/Br dual-doped Li6PS5Br electrolyte with superior conductivity for all-solid-state batteries,” Scr. Mater., vol. 227, no. 115303, p. 115303, 2023.
  9. M. L. Benea and O. D. Benea, “Mathematical modelling in Matlab of the experimental results shows the electrochemical potential difference - temperature of the WC coatings immersed in a NaCl solution,” IOP Conf. Ser. Mater. Sci. Eng., vol. 106, p. 012025, 2016.
  10. E. A. Dimopoulos et al., “HAYSTAC: A Bayesian framework for robust and rapid species identification in high-throughput sequencing data,” PLoS Comput. Biol., vol. 18, no. 9, p. e1010493, 2022.
  11. J. Haggin, “Easier-To-Use Computer Systems Available to Organic Chemists: New SYNLIB and Chemist’s Personal Software Series systems are usable with personal computers, aim at synthesis chemists,” Chem. Eng. News Archive, vol. 63, no. 34, pp. 13–16, 1985.
  12. Y. Yang, L. Hansen, and A. Baldi, “Suppression of simultaneous Fmoc-his(trt)-OH racemization and Nα-DIC-endcapping in solid-phase peptide synthesis through design of experiments and its implication for an amino acid activation strategy in peptide synthesis,” Org. Process Res. Dev., vol. 26, no. 8, pp. 2464–2474, 2022.
  13. A. Habibizadeh, M. Honarpisheh, and S. Golabi, “Effect of friction stir spot welding parameters on the microstructure and properties of joints between aluminium and copper,” Weld. World, vol. 66, no. 9, pp. 1757–1774, 2022.
  14. A. C. F. Caldana, J. H. P. P. Eustachio, B. Lespinasse Sampaio, M. L. Gianotto, A. C. Talarico, and A. C. da S. Batalhão, “A hybrid approach to sustainable development competencies: the role of formal, informal and non-formal learning experiences,” Int. J. Sustainability Higher Educ., vol. 24, no. 2, pp. 235–258, 2023.
  15. M. Chakroun, S. A. Bouhamed, I. K. Kallel, B. Solaiman, and H. Derbel, “Feature selection based on discriminative power under uncertainty for computer vision applications,” ELCVIA Electron. Lett. Comput. Vis. Image Anal., vol. 21, no. 1, pp. 111–120, 2022.
  16. H. S. Min, “A review on the Penternary compound thin films,” [Online]. Available: [Accessed: 03-Mar-2023].
  17. A. B. Sandie et al., “Observed versus estimated actual trend of COVID-19 case numbers in Cameroon: A data-driven modelling,” Infect. Dis. Model., vol. 8, no. 1, pp. 228–239, 2023.
  18. A. Altay and A. B. Yildiz, “Complete electrical equivalent circuit based modeling and analysis of permanent magnet direct current (DC) motors,” WSEAS Trans. Circuits And Syst., vol. 21, pp. 182–187, 2022.


Author(s) thanks to Dr. Christna Ahler for this research completion and Data validation support.


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


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

Cite this article

Robert Ahmed and Christna Ahler, “A Review on Background and Applications of Machine Learning in Materials Research”, Journal of Computational Intelligence in Materials Science, vol.1, pp. 077-087, 2023. doi: 10.53759/832X/JCIMS202301008.


© 2023 Robert Ahmed and Christna Ahler. 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.