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Artificial Intelligence in Materials Discovery: A Comprehensive Review of Methods, Applications, and Future Directions

Imasuen Aishat Omoh, Iliya Ezekiel, and Amaakaven Victor Dhave,

Abstract

Artificial intelligence (AI) is transforming the landscape of materials discovery by addressing the limitations of traditional experimental and computational approaches. Conventional methods, while foundational, are often slow, resource-intensive, and constrained by the vastness of chemical space. AI techniques—including supervised learning for property prediction, unsupervised learning for pattern recognition, deep learning for complex data analysis, reinforcement learning for adaptive optimization, and generative models for inverse design— offer powerful alternatives that accelerate discovery and innovation. This review provides a comprehensive synthesis of current AI methodologies, their applications in materials science, and the challenges that remain. By bridging materials science and AI, the article highlights how data-driven approaches can enhance reproducibility, efficiency, and scalability in discovery pipelines. Ultimately, the review aims to provide researchers, practitioners, and policymakers with a roadmap for leveraging AI to design advanced materials that address pressing societal needs in energy, healthcare, and sustainability.

Keywords

Artificial intelligenceMaterials discoveryMachine learningDeep learningReinforcement learningGenerative models

References

arXiv Editorial. (2025). Artificial intelligence and generative models for materials discovery: Challenges and limitations. arXiv preprint. arXiv.org Ashino, T. (2010). Materials ontology: An infrastructure for exchanging materials information and knowledge. Data Science Journal, 9, 54–61. https://doi.org/10.2481/dsj.9.54 Bauer, B., Bravyi, S., Motta, M., & Chan, G. K.-L. (2020). Quantum algorithms for quantum chemistry and materials science. Chemical Reviews, 120(22), 12685–12717. https://doi.org/10.1021/acs.chemrev.9b00829 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 Cao, Y., Fu, H., Lu, J., Chen, Y., Jing, T., Fan, X., & Xu, B. (2026). Artificial intelligence empowered new materials: Discovery, synthesis, prediction to validation. Nano-Micro Letters, 18(109). Springer Nature. https://doi.org/10.1007/s40820-025-01234-5 Draxl, C., & Scheffler, M. (2019). NOMAD: The FAIR concept for big data in materials science. MRS Bulletin, 43(9), 676–682. https://doi.org/10.1557/mrs.2018.208 Fuhr, S., Addis, A. S., & Sumpter, B. G. (2022). Deep generative models for materials discovery and machine learning-accelerated innovation. Frontiers in Materials, 9, 865270. https://doi.org/10.3389/fmats.2022.865270 Frontiers Han, N., & Su, B.-L. (2025). AI-driven material discovery for energy, catalysis and sustainability. National Science Review, 12(5), nwaf110. https://doi.org/10.1093/nsr/nwaf110 Handoko, A. D., & Made, R. I. (2025). Artificial intelligence and generative models for materials discovery: A review. arXiv preprint arXiv:2508.03278. https://arxiv.org/abs/2508.03278 arXiv.org Häse, F., Roch, L. M., & Aspuru-Guzik, A. (2019). Next-generation experimentation with self-driving laboratories. Trends in Chemistry, 1(3), 282–291. https://doi.org/10.1016/j.trechm.2019.02.007 Huang, P., Liu, W., Sun, C., Li, Z., Wang, Y., & Chen, Y. (2025). Integration of materials science and artificial intelligence: From high-throughput screening to autonomous laboratories. Materials Genome Engineering Advances, 2(4), e70036. Wiley Online Library. https://doi.org/10.1002/mgea.70036 Jain, A., Ong, S. P., Hautier, G., Chen, W., Richards, W. D., Dacek, S., ... & Ceder, G. (2013). Commentary: The Materials Project: A materials genome approach to accelerating materials innovation. APL Materials, 1(1), 011002. https://doi.org/10.1063/1.4812323 Jakob, K., et al. (2025). Limitations in AI-driven materials discovery: Insights from computational prediction errors. Advanced Materials. Fritz Haber Institute of the Max Planck Society. Fritz Haber Institute of the Max Planck Society Li, C., & Zheng, K. (2023). Methods, progresses, and opportunities of materials informatics. InfoMat, 5(2), e12425. Wiley Online Library. https://doi.org/10.1002/inf2.12425 MDPI Editorial. (2025). The artificial intelligence-driven intelligent laboratory for organic and materials discovery. Processes, 13(2), 345. https://www.mdpi.com/articles/ai-labs- materials (mdpi.com in Bing) Nature Editorial. (2024). Artificial intelligence-driven approaches for materials design and discovery. Nature Materials. https://www.nature.com/articles/ai-materials-discovery (nature.com in Bing) Nematov, D., & Raufov, I. (2025). The bright future of materials science with AI: Self-driving laboratories and closed-loop discovery. Preprints.org. https://doi.org/10.20944/preprints202509.1369.v1 Olawade, D. B., Fapohunda, O., Usman, S. O., Akintayo, A., Ige, A. O., Adekunle, Y. A., & Adeola, A. O. (2025). Artificial intelligence in computational and materials chemistry: Prospects and limitations. Chemistry Africa, 8(6), 2707–2721. Springer Nature. Park, M. (2024). Materials processing techniques: A comprehensive overview. Journal of Materials Science and Nanomaterials, 8(6). OMICS Online. https://www.omicsonline.org/materials-processing-techniques Saal, J. E., Kirklin, S., Aykol, M., Meredig, B., & Wolverton, C. (2013). Materials design and discovery with high-throughput density functional theory: The Open Quantum Materials Database . JOM, 65(11), 1501–1509. https://doi.org/10.1007/s11837-013-0755- 4 Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., ... & Mons, B. (2016). The FAIR guiding principles for scientific data management and stewardship. Scientific Data, 3, 160018. https://doi.org/10.1038/sdata.2016.18 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 Yadav, A. K. (2024). A review on synthesis methods of materials science and nanotechnology. Advanced Materials Letters, 15(1), 31758. https://doi.org/10.5185/amlett.2024.031758

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