Artificial Intelligence-Driven Modeling of Structure–Property Relationships in Doped h-BN and Carbon Foam Materials
Keywords:
artificial intelligence, machine learning, hexagonal boron nitride, carbon foam, structure–property relationships, materials design, system-level architecture, sustainability, fairness, policyAbstract
The engineering of advanced materials such as doped hexagonal boron nitride (h-BN) and carbon foam has traditionally relied on empirical trial-and-error and first-principles simulations that are computationally expensive and limited in their ability to explore vast composition spaces. Recent advances in artificial intelligence, particularly machine learning and deep learning, offer a paradigm shift by enabling rapid, accurate, and interpretable modeling of structure–property relationships. This paper examines the system-level integration of AI-driven modeling into the discovery and optimization of doped h-BN and carbon foam materials. It discusses the architectural choices in representing material structures, the trade-offs between model complexity and generalizability, and the infrastructural requirements for deploying such models across computational and experimental workflows. The analysis extends to sustainability considerations, including the energy footprint of AI training and inference, robustness against data scarcity and distributional shift, and fairness in the distribution of material discovery benefits. Policy implications concerning open data, reproducibility, and equitable access to AI tools are critically assessed. By framing AI-driven materials modeling as a socio-technical system, this paper provides a forward-looking perspective on governance and infrastructure challenges that must be addressed to realize the full potential of these methods in accelerating the development of next-generation doped h-BN and carbon foam materials for applications in gas sensing, thermal management, and energy storage.
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.
2. Choi, S., Lee, J., & Kim, S. (2022). Graph neural networks for materials science: A review. npj Computational Materials, 8, 119.
3. Gómez-Bombarelli, R., Wei, J. N., Duvenaud, D., Hernández-Lobato, J. M., Sánchez-Lengeling, B., Sheberla, D., ... & Aspuru-Guzik, A. (2018). Automatic chemical design using a data-driven continuous representation of molecules. ACS Central Science, 4(2), 268–276.
4. Hautier, G., Fischer, C. C., Jain, A., & Ceder, G. (2010). Finding nature’s missing ternary oxide compounds using machine learning and density functional theory. Chemistry of Materials, 22(12), 3762–3767.
5. Himanen, L., Geurts, A., Foster, A. S., & Rinke, P. (2019). Data-driven materials science: Status, challenges, and perspectives. Advanced Science, 6(21), 1900808.
6. 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.
7. Jørgensen, P. B., & Bhowmik, A. (2020). A review of machine learning for materials science: From prediction to design. Digital Discovery, 1(1), 1–20.
8. Kirklin, S., Saal, J. E., Meredig, B., Thompson, A., Doak, J. W., Aykol, M., ... & Wolverton, C. (2015). The Open Quantum Materials Database (OQMD): Assessing the accuracy of DFT formation energies. npj Computational Materials, 1, 15010.
9. Kumar, S., Singh, R., & Sharma, M. (2021). Machine learning for property prediction of carbon-based materials: A review. Journal of Materials Science, 56(14), 8695–8730.
10. Li, H., & Chen, Y. (2023). Transfer learning in materials informatics: A survey. MRS Communications, 13(4), 687–699.
11. Ma, J., & Guo, Y. (2020). Active learning for materials science: A review. Computational Materials Science, 174, 109479.
12. Meredig, B., Agrawal, A., Kirklin, S., Saal, J. E., Doak, J. W., Thompson, A., ... & Wolverton, C. (2014). Combinatorial screening for new materials in unconstrained composition space with machine learning. Physical Review B, 89(9), 094104.
13. Raccuglia, P., Elbert, K. C., Adler, P. D., Falk, C., Wenny, M. B., Mollo, A., ... & Zeller, M. (2016). Machine-learning-assisted materials discovery using failed experiments. Nature, 533(7601), 73–76.
14. Zhao, J., Zhang, M., Wang, C., Yu, W., Zhu, Y., & Zhu, P. (2025). First-principles study of CO, NH3, HCN, CNCl, and Cl2 gas adsorption behaviors of metal and cyclic C–metal B-and N-site-doped h-BNs. Electronic Materials Letters, 21(2), 268-288.
15. Al‐Majali, M. R., Zhang, M., Al‐Majali, Y. T., & Trembly, J. P. (2025). Impact of raw material on thermo‐physical properties of carbon foam. The Canadian Journal of Chemical Engineering, 103(3), 1309-1318.
16. Ramprasad, R., Batra, R., Pilania, G., Mannodi-Kanakkithodi, A., & Kim, C. (2017). Machine learning in materials informatics: Recent applications and prospects. npj Computational Materials, 3, 54.
17. Rupp, M., Tkatchenko, A., Müller, K. R., & von Lilienfeld, O. A. (2012). Fast and accurate modeling of molecular atomization energies with machine learning. Physical Review Letters, 108(5), 058301.
18. Schopp, R., & Maass, P. (2022). Uncertainty quantification in machine learning for materials science. Journal of Chemical Physics, 156(8), 084106.
19. Sun, W., & Ceder, G. (2020). Bayesian optimization for materials design. MRS Bulletin, 45(12), 1024–1031.
20. Ward, L., Agrawal, A., Choudhary, A., & Wolverton, C. (2016). A general-purpose machine learning framework for predicting properties of inorganic materials. npj Computational Materials, 2, 16028.
21. Wei, J. N., & Aspuru-Guzik, A. (2020). Deep learning in chemistry: The new frontier. Nature Chemistry, 12(1), 5–7.
22. 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.
23. Zhang, Y., & Ling, C. (2021). Green machine learning for sustainable materials discovery. Nature Computational Science, 1(9), 576–584.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Journal of Data Intelligence and AI Systems
This work is licensed under a Creative Commons Attribution 4.0 International License.
This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
