Role of Artificial Intelligence (AI) in Industry Using Chemical Applications

Authors

Muskan Khan

Department of Chemistry, Maa Shakumbhari University, Saharanpur, U.P (India)

Gitika Saini

Department of Chemistry, Maa Shakumbhari University, Saharanpur, U.P (India)

Raj Kumar

Department of Chemistry, Maa Shakumbhari University, Saharanpur, U.P (India)

Krishna Anand

Department of Chemistry, Maa Shakumbhari University, Saharanpur, U.P (India)

Article Information

DOI: 10.51584/IJRIAS.2026.110400108

Subject Category: Education

Volume/Issue: 11/4 | Page No: 1474-1483

Publication Timeline

Submitted: 2026-04-16

Accepted: 2026-04-21

Published: 2026-05-12

Abstract

Artificial Intelligence (AI) is transforming the chemical industry by enhancing efficiency, safety, and sustainability. This paper explores the role of AI in industrial chemical applications, including process optimization, predictive maintenance, drug discovery, and green chemistry. By integrating machine learning algorithms with chemical processes, industries can reduce costs, improve product quality, and minimize environmental impact. The study proposes a conceptual AI-driven framework for real-time monitoring and optimization of chemical processes. Furthermore, it highlights current challenges such as data limitations, high implementation costs, and lack of skilled workforce. The paper concludes with future directions emphasizing the importance of AI in achieving sustainable industrial growth.

Keywords

Artificial Intelligence (AI), Chemical Industry, Machine learning, Deep Learning

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References

1. 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. [Google Scholar] [Crossref]

2. Carvalho, T. P., et al. (2019). A systematic literature review of machine learning methods applied to predictive maintenance. Computers & Industrial Engineering, 137, 106024. [Google Scholar] [Crossref]

3. Coley, C. W., et al. (2018). Machine learning in computer-aided synthesis planning. Accounts of Chemical Research, 51(5), 1281–1289. [Google Scholar] [Crossref]

4. Jablonka, K. M., et al. (2021). Big-data science in porous materials. Nature Reviews Materials, 6(10), 1–20. [Google Scholar] [Crossref]

5. Jardine, A. K. S., Lin, D., & Banjevic, D. (2006). A review on machinery diagnostics and prognostics. Mechanical Systems and Signal Processing, 20(7), 1483–1510. [Google Scholar] [Crossref]

6. Lee, J., Kao, H. A., & Yang, S. (2014). Service innovation and smart analytics for Industry 4.0. Procedia CIRP, 16, 3–8. [Google Scholar] [Crossref]

7. Mak, K. K., & Pichika, M. R. (2019). Artificial intelligence in drug development: present status and future prospects. Drug Discovery Today, 24(3), 773–780. [Google Scholar] [Crossref]

8. Paul, D., et al. (2021). Artificial intelligence in drug discovery and development. Drug Discovery Today, 26(1), 80–93. [Google Scholar] [Crossref]

9. Ramprasad, R., et al. (2017). Machine learning in materials informatics. npj Computational Materials, 3(1), 1–13. [Google Scholar] [Crossref]

10. Rolnick, D., et al. (2019). Tackling climate change with machine learning. Nature Climate Change, 9(6), 1–9. [Google Scholar] [Crossref]

11. Schwaller, P., et al. (2021). Prediction of chemical reaction yields using deep learning. ACS Central Science, 7(3), 1–10. [Google Scholar] [Crossref]

12. Segler, M. H. S., & Waller, M. P. (2017). Neural-symbolic machine learning for retrosynthesis. Chemical Science, 8(7), 1–9. [Google Scholar] [Crossref]

13. Zhavoronkov, A., et al. (2019). Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nature Biotechnology, 37(9), 1038–1040. [Google Scholar] [Crossref]

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