Advancements in Materials Science: AI-Powered Predictions of Mechanical Behaviours in Natural Fiber Polymer Composites
Mohammed MOHAMMED, Jawad K. OLEIWI, Aeshah M. MOHAMMED, Azlin F. OSMAN, Tijjani ADAM, Bashir O. BETAR, Subash C. B. GOPINATH, Kuzmin ANTON
Abstract. The discipline of materials science is advancing quickly due to the incorporation of artificial intelligence (AI) to forecast the mechanical properties of natural fibre polymer composites. These composites are well-known for being environmentally friendly and having excellent mechanical qualities, making them crucial in many industrial applications. The intricacy and variety of their qualities make precisely forecasting their mechanical behaviour challenging. Conventional empirical and experimental methods are helpful but frequently do not fully capture the intricate interactions inside these materials. The review paper explores the revolutionary progress in AI-driven prediction models that offer to surpass these constraints. We thoroughly investigate several AI approaches, such as machine learning and deep learning, used to predict the mechanical characteristics of natural fibre polymer composites with exceptional accuracy and efficiency. We emphasise the advancements in prediction capacities brought forth by AI via a thorough examination of current case examples. We discuss the difficulties faced when incorporating AI into materials science, such as limited data and sophisticated models, and suggest creative methods and cross-disciplinary partnerships to tackle these obstacles. The consequences of AI-driven predictions are significant, providing a thorough comprehension of material behaviours and enabling the sustainable creation of novel composites with enhanced features. In the future, we anticipate a growing range of AI applications in materials science, leading to the creation of new materials and improvements in predictive modelling methods. This paper highlights the interdependent connection between artificial intelligence and materials science, signalling a new age in the anticipatory examination of natural fibre polymer composites.
Keywords
AI in Materials Science, Natural Fiber Composites, Predictive Modeling, Sustainability, Smart Composites
Published online 7/15/2026, 9 pages
Copyright © 2026 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Mohammed MOHAMMED, Jawad K. OLEIWI, Aeshah M. MOHAMMED, Azlin F. OSMAN, Tijjani ADAM, Bashir O. BETAR, Subash C. B. GOPINATH, Kuzmin ANTON, Advancements in Materials Science: AI-Powered Predictions of Mechanical Behaviours in Natural Fiber Polymer Composites, Materials Research Proceedings, Vol. 68, pp 81-89, 2026
DOI: https://doi.org/10.21741/9781644904237-11
The article was published as article 11 of the book Green Materials, Processing & Characterization
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
References
[1] L. Mohammed, M. Ansari, G. Pua, M. Jawaid, & S. Islam, A review on natural fiber reinforced polymer composite and its applications, International Journal of Polymer Science 2015 (2015) 1-15. https://doi.org/10.1155/2015/243947
[2] I. A. S. Salem, A. R. Rozyanty, B. O. Betar, T. Adam, Mohammed, M., & A. M. Mohammed, Study of the effect of surface treatment of kenaf fiber on chemical structure and water absorption of kenaf filled unsaturated polyester composite, Journal of Physics: Conference Series 908 (2017) 012001. https://doi.org/10.1088/1742-6596/908/1/012001
[3] P. Kluge and S. Eichhorn, Calculation concept for wood‐based components in mechanical engineering, Advanced Engineering Materials 25(15) (2023). https://doi.org/10.1002/adem.202300085
[4] Y. Sun, J. Zhang, G. Li, Y. Wang, J. Sun, & C. Jiang, Optimized neural network using beetle antennae search for predicting the unconfined compressive strength of jet grouting coalcretes, Geomechanics 43(4) (2019) 801-813. https://doi.org/10.1002/nag.2891
[5] S. Zhang, Y. Wang, Y. Zheng, Q. Li, J. Huang & X. Long, Artificial intelligence in melanoma: a systematic review. Journal of Cosmetic Dermatology 21 (2022) 5993-6004. https://doi.org/10.1111/jocd.15323
[6] M. Mohammed, J. K. Oleiwi, A. M. Mohammed, A. J. A. M. Jawad, A. F. Osman, T. Adam & M. Jaafar, Comprehensive Insights on Mechanical Attributes of Natural-Synthetic Fibres in Polymer Composites, Journal of Materials Research and Technology (2023). https://doi.org/10.1016/j.jmrt.2023.06.148
[7] M. M. Mohammed, A. M. Rasidi, R. B. Mohammed, R. Rahman, A. F. Osman, T. Adam & O. S. Dahham, Interfacial bonding mechanisms of natural fibre-matrix composites: an overview, BioResources, 17(4) (2022) 7031. https://doi.org/10.15376/biores.17.4.Mohammed
[8] M. Mohammed, A. R. Rozyanty, T. Adam, & B. O. Betar, Study of the weathering effect in a natural environment on the hybrid kenaf bast/glass fibre-filled unsaturated polyester composite, AIP Conference Proceedings 1885 (2017). https://doi.org/10.1063/1.5002395
[9] F. Bini, A. Pica, L. Azzimonti, A. Giusti, L. Ruinelli, F. Marinozzi & P. Trimboli, Artificial intelligence in thyroid field a comprehensive review, Cancers 13(19) (2021) 4740. https://doi.org/10.3390/cancers13194740
[10] K. Butler, D. Davies, H. Cartwright, O. Isayev & A. Walsh, Machine learning for molecular and materials science, Nature 559 (2018) 547-555. https://doi.org/10.1038/s41586-018-0337-2
[11] D. Elton, Z. Boukouvalas, M. Butrico, M. Fuge & P. Chung, Applying machine learning techniques to predict the properties of energetic materials, Scientific Reports 8(1) (2018). https://doi.org/10.1038/s41598-018-27344-x
[12] J. Carolan, J. McGonigle, A. Dennis, P. Lorgelly & A. Banerjee, Technology-enabled, evidence-driven, and patient-centered: the way forward for regulating software as a medical device, Jmir Medical Informatics 10(1) (2022) e34038. https://doi.org/10.2196/34038
[13] B. Sheng, X. Chen, T. Li, T. Ma, Y. Yang, L. Bi & M. Zhang, An overview of artificial intelligence in diabetic retinopathy and other ocular diseases, Frontiers in Public Health, 10 (2022). https://doi.org/10.3389/fpubh.2022.971943
[14] G. Fulk, Artificial Intelligence and Neurologic Physical Therapy, Journal of Neurologic Physical Therapy, 47(1) (2023) 1-2. https://doi.org/10.1097/NPT.0000000000000426
[15] R. Miotto, F. Wang, S. Wang, X. Jiang & J. Dudley, Deep learning for healthcare: review, opportunities and challenges, Briefings in Bioinformatics 19(6) (2017) 1236-1246. https://doi.org/10.1093/bib/bbx044
[16] G. Pilania, C. Iverson, T. Lookman & B. Marrone, Machine-learning-based predictive modeling of glass transition temperatures: a case of polyhydroxyalkanoate homopolymers and copolymers, Journal of Chemical Information and Modeling 59(12) (2019) 5013-5025. https://doi.org/10.1021/acs.jcim.9b00807
[17] J. Huang, J Liew, A. Ademiloye & K. Liew, Artificial intelligence in materials modeling and design, Archives of Computational Methods in Engineering 28(5) (2020) 3399-3413. https://doi.org/10.1007/s11831-020-09506-1
[18] W. Sha, Y. Guo, Y. Qing, S. Tang, X. Zhang, S. Lu & X. Ai, Artificial intelligence to power the future of materials science and engineering, Advanced Intelligent Systems 2(4) (2020). https://doi.org/10.1002/aisy.201900143
