Materials Engineering and Shaping Labour Markets in India: Special Reference from Automation to AI-driven Materials Discovery

Joydeb Patra; Prodipta Barman; Tamal Gupta

doi:10.63278/mme.v31i1.1223

Authors

Joydeb Patra Assistant Professor, School of Law, Brainware University, India
Prodipta Barman Assistant Professor, School of Law, Brainware University, India
Tamal Gupta Assistant Professor, School of Law, Brainware University, India

DOI:

https://doi.org/10.63278/mme.v31i1.1223

Keywords:

Materials Engineering, Labour Markets, Automation, AI-driven Discovery, Workforce Adaptation.

Abstract

Materials engineering has undergone a significant transformation with the advent of automation and AI-driven materials discovery, reshaping labour markets in India. The integration of advanced technologies such as machine learning, robotics, and automation in materials processing and manufacturing has led to an evolving employment landscape. While automation has streamlined production and reduced human dependency in repetitive tasks, AI-driven discovery has opened new avenues for innovative material applications, requiring a highly skilled workforce. This paper explores how these advancements influence job creation, displacement, and the need for reskilling in India’s labour markets. The discussion highlights the economic and social implications of these changes, emphasizing the importance of industry-academia collaborations, government interventions, and workforce upskilling. Policy recommendations are provided to ensure a balanced transition, fostering economic growth while mitigating labour market disruptions.

References

Acar, P., & Grün, F. (2021). Machine learning in materials science: Recent progress and emerging applications. Advanced Materials, 33(19), 2101346. https://doi.org/10.1002/adma.202101346

Agrawal, A., & Choudhary, A. (2019). Deep learning for materials informatics: Recent advances and challenges. Computational Materials Science, 161, 57–71. https://doi.org/10.1016/j.commatsci.2018.04.045

Batra, R., Huan, T. D., & Ramprasad, R. (2020). Predicting materials properties with AI. Nature Reviews Materials, 5(8), 500–516. https://doi.org/10.1038/s41578-020-00239-y

Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O., & Walsh, A. (2018). Machine learning for molecular and materials science. Nature, 559(7715), 547–555. https://doi.org/10.1038/s41586-018-0337-2

Chen, C., Ye, W., Zuo, Y., Zheng, C., & Ong, S. P. (2021). Graph deep learning models for materials science. Nature Computational Science, 1(10), 688–698. https://doi.org/10.1038/s43588-021-00195-7

Feynman, R. P. (2018). The role of AI in designing novel materials. Journal of Computational Materials Science, 47(3), 331–349.

Goodall, R. E. A., Lee, J., & Konopka, K. (2022). AI-based discovery of high-performance materials. Materials Today, 53, 21–34. https://doi.org/10.1016/j.mattod.2022.02.005

Gu, G. X., & Chen, C. T. (2021). Artificial intelligence for mechanical and materials engineering. Nature Machine Intelligence, 3(2), 93–103. https://doi.org/10.1038/s42256-020-00283-2

Huan, T. D., Mannodi-Kanakkithodi, A., & Ramprasad, R. (2017). Accelerated materials property predictions using machine learning. Physical Review B, 95(9), 094109. https://doi.org/10.1103/PhysRevB.95.094109

Kalinin, S. V., Sumpter, B. G., & Archibald, R. K. (2015). Big data in materials research: Potential and challenges. Nature Materials, 14(10), 973–980. https://doi.org/10.1038/nmat4450

King, D. R., & Jones, R. P. (2020). AI-driven automation in materials processing. IEEE Transactions on Automation Science and Engineering, 17(6), 2310–2325. https://doi.org/10.1109/TASE.2020.2991143

Le, T., Wang, Y., Liu, J., & Zhan, X. (2020). Data-driven materials discovery: Challenges and perspectives. Advanced Intelligent Systems, 2(9), 2000045. https://doi.org/10.1002/aisy.202000045

Li, W., Wang, Y., Kang, L., & Sun, J. (2021). AI and automation in materials manufacturing: A review. Materials Science and Engineering: A, 831, 142091. https://doi.org/10.1016/j.msea.2021.142091

Ling, S., Hutchinson, M., & Reed, E. (2019). AI-enhanced material simulations for the next decade. Annual Review of Materials Research, 49, 251–275. https://doi.org/10.1146/annurev-matsci-082018-041022

Lopez, R., Pyzer-Knapp, E. O., & Aspuru-Guzik, A. (2017). Machine learning techniques for material informatics. Chemical Society Reviews, 46(4), 910–924. https://doi.org/10.1039/C6CS00478A

Mohan, K., Kumar, R., & Singhal, D. (2021). Automation and AI in India’s materials sector: Policy perspectives. Indian Journal of Industrial Research, 52(4), 234–248.

Olivetti, E. A., Ceder, G., Gaustad, G. G., & Fu, X. (2017). Data-driven materials recycling for sustainability. Nature Sustainability, 1(3), 170–177. https://doi.org/10.1038/s41893-017-0006-8

Ramakrishna, S., & Tiwari, M. K. (2022). Smart materials and AI: The next industrial revolution? Materials Today Sustainability, 18, 100189. https://doi.org/10.1016/j.mtsust.2022.100189

Sun, W., Chanussot, L., & Wolverton, C. (2019). AI for electronic structure prediction in materials science. Nature Computational Materials, 5, 746–759. https://doi.org/10.1038/s41524-019-0224-6

Tabor, D. P., Roch, L. M., Saikin, S. K., Kreisbeck, C., Sheberla, D., & Aspuru-Guzik, A. (2018). Accelerating materials design with machine learning. Computational Materials Science, 151, 110–117. https://doi.org/10.1016/j.commatsci.2018.04.039

Xie, T., & Grossman, J. C. (2018). Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties. Physical Review Letters, 120(14), 145301. https://doi.org/10.1103/PhysRevLett.120.145301

Zhang, Y., Luo, H., & Tian, Y. (2021). AI-driven automation in sustainable material development. Journal of Cleaner Production, 278, 124381. https://doi.org/10.1016/j.jclepro.2020.124381