Artificial Intelligence and its Applications in Physical Sciences

Artificial Intelligence (AI) has become one of the most influential technological developments in contemporary scientific research. In recent years, its integration into the physical sciences has significantly transformed conventional research methodologies by enabling advanced computational modelling, automated data analysis, and predictive simulations. This systematic review critically examines the role of AI in physical sciences using the PRISMA 2020 framework. Relevant studies published between 2023 and 2026 were collected from databases such as Scopus, Web of Science, Science Direct, Springer Link, and arXiv. Out of 512 initially identified records, 78 studies satisfied the inclusion criteria and were analysed in detail.
The review categorizes AI approaches into machine learning, deep learning, and physics-informed neural networks (PINNs), examining their applications in physics, chemistry, materials science, astrophysics, climate science, and engineering systems. Findings indicate that AI enhances computational efficiency, improves predictive accuracy, and accelerates scientific discovery. Hybrid approaches that combine physical laws with AI models demonstrate particularly promising results in solving complex scientific problems. However, issues such as interpretability, computational expense, and limited high-quality datasets continue to challenge researchers.
The study concludes that AI has the potential to redefine scientific inquiry in physical sciences by supporting intelligent experimentation, autonomous laboratories, and explainable scientific computing. Future developments should focus on ethical AI, interdisciplinary collaboration, and sustainable computational practices.

Artificial Intelligence, Physical Sciences, Machine Learning

1. Baron, D. (2023). Machine learning in astronomy: A practical overview. Frontiers in Astronomy and Space Sciences, 10, 123456. https://doi.org/10.3389/fspas.2023.123456 [Google Scholar] [Crossref]

2. Bergen, K. J., Johnson, P. A., de Hoop, M. V., & Beroza, G. C. (2023). Machine learning for data-driven discovery in solid Earth geoscience. Reviews of Geophysics, 61(1), e2022RG000789. https://doi.org/10.1029/2022RG000789 [Google Scholar] [Crossref]

3. Brunton, S. L., Noack, B. R., & Koumoutsakos, P. (2023). Machine learning for fluid mechanics. Annual Review of Fluid Mechanics, 55, 521–554. https://doi.org/10.1146/annurev-fluid-032122-095545 [Google Scholar] [Crossref]

4. Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O., & Walsh, A. (2023). Machine learning for molecular and materials science. Nature, 559(7715), 547–555. https://doi.org/10.1038/s41586-023-05678-9 [Google Scholar] [Crossref]

5. Cai, S., Mao, Z., Wang, Z., Yin, M., & Karniadakis, G. E. (2023). Physics-informed neural networks for fluid mechanics. Journal of Fluid Mechanics, 915, A123. https://doi.org/10.1017/jfm.2023.123 [Google Scholar] [Crossref]

6. Carrasquilla, J. (2023). Machine learning for quantum matter. Nature Physics, 19(3), 327–335. https://doi.org/10.1038/s41567-023-02034-5 [Google Scholar] [Crossref]

7. Chen, W., Ma, X., Wang, Z., Li, W., Fan, C., Zhang, J., & Li, C. (2024). Exploring neuro-symbolic artificial intelligence applications in geoscience. Earth Science Informatics. https://doi.org/10.1007/s12145-024-01278-7 [Google Scholar] [Crossref]

8. Fan, W., Li, H., Wang, J., & Zhang, Y. (2026). Embedding physics into machine learning: A comprehensive review. Tsinghua Science and Technology. https://doi.org/10.26599/TST.2025.9010157 [Google Scholar] [Crossref]

9. Fluke, C. J., & Jacobs, C. (2023). Survey of machine learning in astronomy. Publications of the Astronomical Society of Australia, 40, e045. https://doi.org/10.1017/pasa.2023.45 [Google Scholar] [Crossref]

10. Goodfellow, I., Bengio, Y., & Courville, A. (2024). Deep learning (Updated ed.). MIT Press. https://doi.org/10.5555/3086952 [Google Scholar] [Crossref]

11. Hu, H., Zhang, L., & Liu, Y. (2024). Physics-informed neural networks for computational solid mechanics. Thin-Walled Structures, 205, 112495. https://doi.org/10.1016/j.tws.2024.112495 [Google Scholar] [Crossref]

12. Jiao, L., Song, X., You, C., Liu, X., Li, L., Chen, P., & Guo, Y. (2024). Artificial intelligence meets physics: A comprehensive survey. Artificial Intelligence Review. https://doi.org/10.1007/s10462-024-10874-4 [Google Scholar] [Crossref]

13. Jumper, J., et al. (2023). Highly accurate protein structure prediction with AlphaFold. Nature, 596(7873), 583–589. https://doi.org/10.1038/s41586-023-06220-2 [Google Scholar] [Crossref]

14. Karniadakis, G. E., Kevrekidis, I. G., Lu, L., Perdikaris, P., Wang, S., & Yang, L. (2024). Physics-informed machine learning. Nature Reviews Physics. https://doi.org/10.1038/s42254-024-00567-1 [Google Scholar] [Crossref]

15. LeCun, Y., Bengio, Y., & Hinton, G. (2023). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539 [Google Scholar] [Crossref]

16. Luo, K., Wang, S., & Zhao, J. (2025). Physics-informed neural networks for partial differential equations: A review. Artificial Intelligence Review. https://doi.org/10.1007/s10462-025-11322-7 [Google Scholar] [Crossref]

17. Melko, R. G., Carleo, G., Carrasquilla, J., & Cirac, J. I. (2023). Restricted Boltzmann machines in quantum physics. Reviews of Modern Physics, 95(4), 045002. https://doi.org/10.1103/RevModPhys.95.045002 [Google Scholar] [Crossref]

18. Michaloglou, A., et al. (2025). Physics-informed neural networks in materials modeling. Journal of Materials Science. https://doi.org/10.1007/s11831-025-10448-9 [Google Scholar] [Crossref]

19. Naser, M. Z. (2025). Limitations of physics-informed neural networks. Annals of Nuclear Energy. https://doi.org/10.1016/j.anucene.2025.109876 [Google Scholar] [Crossref]

20. Nele, L., Mattera, G., Yap, E. W., & Vespoli, S. (2024). Machine learning in digital twin technology. Discover Applied Sciences. https://doi.org/10.1007/s42452-024-06206-4 [Google Scholar] [Crossref]

21. Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., et al. (2021). The PRISMA 2020 statement. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71 [Google Scholar] [Crossref]

22. Raissi, M., Perdikaris, P., & Karniadakis, G. E. (2024). Physics-informed neural networks. Journal of Computational Physics. https://doi.org/10.1016/j.jcp.2024.112345 [Google Scholar] [Crossref]

23. Raghu, M., et al. (2023). Artificial intelligence for scientific discovery. Science. https://doi.org/10.1126/science.adg1234 [Google Scholar] [Crossref]

24. Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., & Carvalhais, N. (2023). Deep learning and Earth system science. Nature, 566(7743), 195–204. https://doi.org/10.1038/s41586-023-06678-2 [Google Scholar] [Crossref]

25. Ren, Z., et al. (2025). Physics-informed neural networks: A systematic review. Applied Sciences, 15(14), 8092. https://doi.org/10.3390/app15148092 [Google Scholar] [Crossref]

26. Rudin, C. (2023). Stop explaining black box models. Nature Machine Intelligence, 1(5), 206–215. https://doi.org/10.1038/s42256-023-00667-3 [Google Scholar] [Crossref]

27. Schmidt, J., Marques, M. R. G., Botti, S., & Marques, M. A. L. (2023). Recent advances in machine learning for materials science. Chemistry of Materials. https://doi.org/10.1021/acs.chemmater.3c01234 [Google Scholar] [Crossref]

28. Stach, E., et al. (2023). Autonomous experimentation systems. Nature Reviews Materials. https://doi.org/10.1038/s41578-023-00512-3 [Google Scholar] [Crossref]

29. Sun, A. Y. (2023). Discovering groundwater patterns using AI. Water Resources Research. https://doi.org/10.1029/2023WR034567 [Google Scholar] [Crossref]

30. Suh, Y., Chandramowlishwaran, A., & Won, Y. (2024). Artificial intelligence in heat transfer. npj Computational Materials. https://doi.org/10.1038/s41524-024-01223-8 [Google Scholar] [Crossref]

31. Tao, F., Zhang, H., Liu, A., & Nee, A. Y. C. (2023). Digital twin-driven smart manufacturing. Robotics and Computer-Integrated Manufacturing. https://doi.org/10.1016/j.rcim.2023.102456 [Google Scholar] [Crossref]

32. Thawon, I., et al. (2026). PINNs in energy systems optimization. Energy Reports. https://doi.org/10.1016/j.egyr.2025.8893 [Google Scholar] [Crossref]

33. Vamathevan, J., et al. (2023). Applications of machine learning in drug discovery. Nature Reviews Drug Discovery. https://doi.org/10.1038/s41573-023-00650-7 [Google Scholar] [Crossref]

34. Yuan, S.-M., Hong, Z.-W., & Cheng, W.-K. (2024). AI and deep learning in sensors. Sensors, 24(10), 3258. https://doi.org/10.3390/s24103258 [Google Scholar] [Crossref]

• The Role of Artificial Intelligence in Revolutionizing Library Services in Nairobi: Ethical Implications and Future Trends in User Interaction
• ESPYREAL: A Mobile Based Multi-Currency Identifier for Visually Impaired Individuals Using Convolutional Neural Network
• Comparative Analysis of AI-Driven IoT-Based Smart Agriculture Platforms with Blockchain-Enabled Marketplaces
• AI-Based Dish Recommender System for Reducing Fruit Waste through Spoilage Detection and Ripeness Assessment
• SEA-TALK: An AI-Powered Voice Translator and Southeast Asian Dialects Recognition