DOI: https://doi.org/10.32515/2664-262X.2025.12(43).2.134-142
Artificial Intelligence in Materials Research: Trends, Tools, and Transformations
About the Authors
Yurii Kovalov, Associate Professor, PhD (Candidate of Technics Sciences), Associate Professor of the Department of Materials Science and Foundry Production, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, ORCID: https://orcid.org/0000-0002-1729-2033, e-mail: yukovalyov@ukr.net
Viktor Aulin, Professor, Doctor of Technical Sciences, Professor of the Department of Operation and Repair of Machines, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, ORCID: https://orcid.org/0000-0003-2737-120X, e-mail: aulinvv@gmail.com
Serhii Kovalov, PhD in Pedagogy (Candidate of Pedagogical Sciences), Associate Professor of the Department of Higher Mathematics and Physics, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, ORCID: https://orcid.org/0009-0002-3922-8697, e-mail: kovalyovserggr@ukr.net
Oleksandr Kuzyk, Assoc. Prof., PhD (Candidate of Technics Sciences), Head of the Department of Materials Science and Foundry Production, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, ORCID: https://orcid.org/0000-0002-3047-3760, e-mail: kuzykov1985@gmail.com.
Andrii Hrynkiv, Senior Researcher, PhD in Technics (Candidate of Technics Sciences), Senior Lecturer of the Department of Machinery Operation and Repair, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, ORCID: https://orcid.org/0000-0002-4478-1940, е-mail: AVGrinkiv@gmail.com
Abstract
This paper examines the evolving role of digital platforms in reshaping materials science through the lens of materials informatics. It focuses on three prominent initiatives—Materials Project, NOMAD Repository, and Citrine Informatics—that illustrate how artificial intelligence, computational modeling, and structured data infrastructures are converging to accelerate discovery and innovation. Each platform integrates machine learning algorithms, simulation tools, and data management systems to support tasks such as high-throughput screening, property prediction, and compositional optimization.
The study provides a comparative analysis of the functional architecture of these platforms, identifying core components that could inform the development of a national ecosystem for materials informatics. Particular attention is given to the principles of FAIR data (Findable, Accessible, Interoperable, Reusable), which underpin transparency, reproducibility, and collaborative research across borders. By examining how these platforms operationalize FAIR principles, the paper highlights pathways for aligning Ukrainian research infrastructure with international standards.
Traditional experimental approaches, while foundational, often face limitations in terms of scalability, cost, and time. The integration of AI-driven methods offers a promising alternative, enabling researchers to simulate, predict, and refine materials with greater efficiency. This shift calls for strategic investment not only in technical infrastructure but also in education. The paper proposes the development of interdisciplinary academic programs that combine materials science with data analytics, computational physics, and AI literacy.
Furthermore, the research outlines strategic directions for incorporating Ukrainian materials data into global platforms, emphasizing the importance of two-way knowledge exchange. Such integration would enhance the visibility of Ukrainian research and foster deeper international collaboration. The insights presented here aim to support both academic inquiry and policy development, offering a foundation for future initiatives in sustainable innovation and digital transformation within the materials science domain.
Keywords
materials science, artificial intelligence methods, digital platforms, Materials Project, NOMAD, Citrine Informatics, digital ecosystems
Artificial Intelligence in Materials Research: Trends, Tools, and Transformations
About the Authors
Yurii Kovalov, Associate Professor, PhD (Candidate of Technics Sciences), Associate Professor of the Department of Materials Science and Foundry Production, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, ORCID: https://orcid.org/0000-0002-1729-2033, e-mail: yukovalyov@ukr.net
Viktor Aulin, Professor, Doctor of Technical Sciences, Professor of the Department of Operation and Repair of Machines, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, ORCID: https://orcid.org/0000-0003-2737-120X, e-mail: aulinvv@gmail.com
Serhii Kovalov, PhD in Pedagogy (Candidate of Pedagogical Sciences), Associate Professor of the Department of Higher Mathematics and Physics, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, ORCID: https://orcid.org/0009-0002-3922-8697, e-mail: kovalyovserggr@ukr.net
Oleksandr Kuzyk, Assoc. Prof., PhD (Candidate of Technics Sciences), Head of the Department of Materials Science and Foundry Production, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, ORCID: https://orcid.org/0000-0002-3047-3760, e-mail: kuzykov1985@gmail.com.
Andrii Hrynkiv, Senior Researcher, PhD in Technics (Candidate of Technics Sciences), Senior Lecturer of the Department of Machinery Operation and Repair, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, ORCID: https://orcid.org/0000-0002-4478-1940, е-mail: AVGrinkiv@gmail.com
Abstract
Keywords
Full Text:
PDFReferences
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. doi.org/10.1038/s41586-018-0337-2.
2. Ward, L., Liu, R., Krishna, A., Hegde, V. I., Agrawal, A., Choudhary, A., & Wolverton, C. (2017). Including crystal structure attributes in machine learning models of formation energies via Voronoi tessellations. Physical Review B, 96(2), 024104. https://doi.org/10.1103/PhysRevB.96.024104.
3. Schmidt, J., Marques, M. R. G., Botti, S., & Marques, M. A. L. (2019). Recent advances and applications of machine learning in solid-state materials science. npj Computational Materials, 5(1), 83. https://doi.org/10.1038/s41524-019-0221-0.
4. Draxl, C., & Scheffler, M. (2019). NOMAD: The FAIR concept for big data-driven materials science. MRS Bulletin, 44(7), 570–576. https://doi.org/10.48550/arXiv.1805.05039.
5. Agrawal, A., & Choudhary, A. (2016). Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science. APL Materials, 4(5), 053208. doi.org/10.1063/1.4946894.
6. Citrine Informatics. (2025). AI-powered materials development platform. Retrieved from https://citrine.io/.
7. Nematov, D. & Hojamberdiev, M. (2025). Machine learning-driven materials discovery: Unlocking next- generation functional materials – A review. Comput. Condens. Matter, 45, e01139. doi.org/10.1016/j.cocom.2025.e01139.
8. Aulin, V. V., Kovalov, S. H., Hrynkiv, A. V., Kovalov, Yu. G., Holovaty, A. O., Kuzik, O. V., & Slon, V. V. (2025) Enhancing Tribological System Performance through Intelligent Data Analysis and Predictive Modeling: A Review. Problems of Tribology, 30(3/117), 49–61. https://doi.org/10.31891/2079-1372-2025-117-3-49-61.
9. Aulin, V. V., Hryn’kiv, A. V., & Holovaty, A. O. (2020). Kiberfizychnyy pidkhid do stvorennya, funktsionuvannya ta vdoskonalennya transportnykh ta vyrobnychykh system [Cyber-physical approach to the creation, functioning and improvement of transport-production systems]. Tsentralnoukrainskyi naukovyi visnyk. Tekhnichni nauky, (3)34, 331–343. doi.org/10.32515/2664-262X.2020.3(34).331-343 [in Ukrainian].
10. Aulin, V., Hrynkiv, A., Lysenko, S., Rohovskii, I., Chernovol, M., Lyashuk, O., & Zamota, T. (2019). Studying Truck Transmission Oils Using the Method of Thermal-Oxidative Stability During Vehicle Operation. Eastern-European Journal of Enterprise Technologies, 1(6), 6–12. doi.org/10.15587/1729-4061.2019.156150.
11. 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.
12. Talapatra, A., Boluki, S., Duong, T., Qian, X., Dougherty, E., & Arroyave, R. (2018). Autonomous efficient experiment design for materials discovery with Bayesian model averaging. Phys. Rev. Materials 2, 113803. https://doi.org/10.1103/PhysRevMaterials.2.113803.
13. Ren, F., Ward, L., Williams, T., Laws, K. J., Wolverton, C., Hattrick-Simpers, J., & Mehta, A. (2018). Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments. Science Advances, 4(4), eaaq1566. https://doi.org/10.1126/sciadv.aaq1566.
14. Chen, C., Ye, W., Zuo, Y., Zheng, C., & Ong, S. P. (2020). Graph networks as a universal machine learning framework for molecules and crystals. Nature Communications, 10(1), 357. doi.org/10.1038/s41467-020-19964-7.
15. Tshitoyan, V., Dagdelen, J., Weston, L., Dunn, A., Rong, Z., Kononova, O., Persson, K. A., Ceder, G., & Jain, A. (2019). Unsupervised word embeddings capture latent knowledge from materials science literature. Nature, 571(7763), 95–98. https://doi.org/10.1038/s41586-019-1335-8.
16. Kalidindi, S. R. (2015). Materials data science: Current status and future outlook. Annual Review of Materials Research, 45, 171–193. https://doi.org/10.1146/annurev-matsci-070214-020844.
Citations
1. Butler K. T., Davies D. W., Cartwright H., Isayev O., Walsh A. Machine learning for molecular and materials science. Nature. 2018. Vol. 559, № 7715. P. 547–555. DOI: 10.1038/s41586-018-0337-2.
2. Ward L., Liu R., Krishna A., Hegde V. I., Agrawal A., Choudhary A., Wolverton C. Including crystal structure attributes in machine learning models of formation energies via Voronoi tessellations. Physical Review B. 2017. Vol. 96, № 2. Article 024104. DOI: 10.1103/PhysRevB.96.024104.
3. Schmidt J., Marques M. R. G., Botti S., Marques M. A. L. Recent advances and applications of machine learning in solid-state materials science. npj Computational Materials. 2019. Vol. 5, № 1. Article 83. DOI: 10.1038/s41524-019-0221-0.
4. Draxl C., Scheffler M. NOMAD: The FAIR concept for big data-driven materials science. MRS Bulletin. 2019. Vol. 44, № 7. P. 570–576. DOI: 10.48550/arXiv.1805.05039.
5. Agrawal A., Choudhary A. Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science. APL Materials. 2016. Vol. 4, № 5. Article 053208. DOI: 10.1063/1.4946894.
6. Citrine Informatics. AI-powered materials development platform: веб-сайт. URL: https://citrine.io/ (дата звернення: 12.10.2025)
7. Nematov D., Hojamberdiev M. Machine learning-driven materials discovery: Unlocking next-generation functional materials – A review. Comput. Condens. Matter. 2025. Vol. 45. Article e01139. DOI: 10.1016/j.cocom.2025.e01139.
8. Aulin V. V., Kovalov S. H., Hrynkiv A. V., Kovalov Yu. G., Holovaty A. O., Kuzik O. V., Slon V. V. Enhancing Tribological System Performance through Intelligent Data Analysis and Predictive Modeling: A Review. Problems of Tribology. 2025. Vol. 30, № 3(117). P. 49–61. DOI: 10.31891/2079-1372-2025-117-3-49-61.
9. Аулін В. В., Гриньків А. В., Головатий А. О. Кіберфізичний підхід при створенні, функціонуванні та удосконаленні транспортновиробничих систем. Центральноукраїнський науковий вісник. Технічні науки. 2020. № 3(34). С. 331–343. DOI: 10.32515/2664-262X.2020.3(34).331-343.
10. Aulin V., Hrynkiv A., Lysenko S., Rohovskii I., Chernovol M., Lyashuk O., Zamota T. Studying Truck Transmission Oils Using the Method of Thermal-Oxidative Stability During Vehicle Operation. Eastern- European Journal of Enterprise Technologies. 2019. Vol. 1, № 6. P. 6–12. DOI: 10.15587/1729- 4061.2019.156150.
11. Xie T., Grossman J. C. Crystal Graph Convolutional Neural Networks for an accurate and interpretable prediction of material properties. Physical Review Letters. 2018. Vol. 120, № 14. Article 145301. DOI: 10.1103/PhysRevLett.120.145301.
12. Talapatra A., Boluki S., Duong T., Qian X., Dougherty E., Arroyave R. Autonomous efficient experiment design for materials discovery with Bayesian model averaging. Phys. Rev. Materials. 2018. Vol. 2. Article 113803. DOI: 10.1103/PhysRevMaterials.2.113803.
13. Ren F., Ward L., Williams T., Laws K. J., Wolverton C., Hattrick-Simpers J., Mehta A. Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments. Science Advances. 2018. Vol. 4, № 4. Article eaaq1566. DOI: 10.1126/sciadv.aaq1566.
14. Chen C., Ye W., Zuo Y., Zheng C., Ong S. P. Graph networks as a universal machine learning framework for molecules and crystals. Nature Communications. 2020. Vol. 10, № 1. Article 357. DOI: 10.1038/s41467- 020-19964-7.
15. Tshitoyan V., Dagdelen J., Weston L., Dunn A., Rong Z., Kononova O., Persson K. A., Ceder G., Jain A. Unsupervised word embeddings capture latent knowledge from materials science literature. Nature. 2019. Vol. 571, № 7763. P. 95–98. DOI: 10.1038/s41586-019-1335-8.
16. Kalidindi S. R. Materials data science: Current status and future outlook. Annual Review of Materials Research. 2015. Vol. 45. P. 171–193. DOI: 10.1146/annurev-matsci-070214-020844.
Copyright (©) 2025, Yurii Kovalov, Viktor Aulin, Serhii Kovalov, Oleksandr Kuzyk, Andrii Hrynkiv