Explainable Artificial Intelligence (XAI) for Material Design and Engineering Applications: A Quantitative Computational Framework

Abstract

The advancement of artificial intelligence (AI) in material design and engineering has led to significant improvements in predictive modeling of material properties. However, the lack of interpretability in machine learning (ML)-based material informatics presents a major barrier to its practical adoption. This study proposes a novel quantitative computational framework that integrates ML models with explainable artificial intelligence (XAI) techniques to enhance both predictive accuracy and interpretability in material property prediction. The framework systematically incorporates a structured pipeline, including data processing, feature selection, model training, performance evaluation, explainability analysis, and real-world deployment. It is validated through a representative case study on the prediction of high-performance concrete (HPC) compressive strength, utilizing a comparative analysis of ML models such as Random Forest, XGBoost, Support Vector Regression (SVR), and Deep Neural Networks (DNNs). The results demonstrate that XGBoost achieves the highest predictive performance (R²=0.918), while SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-Agnostic Explanations) provide detailed insights into feature importance and material interactions. Additionally, the deployment of the trained model as a cloud-based Flask-Gunicorn API enables real-time inference, ensuring its scalability and accessibility for industrial and research applications. The proposed framework addresses key limitations of existing ML approaches by integrating advanced explainability techniques, systematically handling nonlinear feature interactions, and providing a scalable deployment strategy. This study contributes to the development of interpretable and deployable AI-driven material informatics, bridging the gap between data-driven predictions and fundamental material science principles.

Keywords

explainable artificial intelligence (XAI) / high-performance concrete / material informatics / predictive modeling / science cloud deployment

References

[1] W. D. Callister, and D. G. Rethwisch, Materials Science and Engineering: An Introduction (John Wiley & Sons, 2020).

[2] M. K. Egbo, “A Fundamental Review on Composite Materials and Some of Their Applications in Biomedical Engineering,” Journal of King Saud University-Engineering Sciences 33, no. 8 (2021): 557–568.

[3] B. Liu, Y. Wang, T. Rabczuk, T. Olofsson, and W. Lu, “Multi-Scale Modeling in Thermal Conductivity of Polyurethane Incorporated With Phase Change Materials Using Physics-Informed Neural Networks,” Renewable Energy 220, no. 1 (2024): 119565, https://doi.org/10.1016/j.renene.2023.119565.

[4] Z. Li, Y. Xu, and D. Huang, “Analytical Solution for Vibration of Functionally Graded Beams With Variable Cross-Sections Resting on Pasternak Elastic Foundations,” International Journal of Mechanical Sciences 191 (2021): 106084.

[5] A. M. Shaaban, C. Anitescu, E. Atroshchenko, and T. Rabczuk, “3D Isogeometric Boundary Element Analysis and Structural Shape Optimization for Helmholtz Acoustic Scattering Problems,” Computer Methods in Applied Mechanics and Engineering 384, no. 2 (2021): 113950, https://doi.org/10.1016/j.cma.2021.113950.

[6] Z. Li, Y. Lu, D. Huang, and T. Rabczuk, “Nonlocal Anisotropic Model for Deformation and Fracture Using Peridynamic Operator Method,” International Journal of Mechanical Sciences 268, no. 14 (2024): 109023, https://doi.org/10.1016/j.ijmecsci.2024.109023.

[7] G. Ceder, “Opportunities and Challenges for First-Principles Materials Design and Applications to Li Battery Materials,” MRS Bulletin 35, no. 9 (2010): 693–701, https://doi.org/10.1557/mrs2010.681.

[8] A. M. Shaaban, C. Anitescu, E. Atroshchenko, and T. Rabczuk, “Isogeometric Boundary Element Analysis and Shape Optimization by PSO for 3D Axi-Symmetric High Frequency Helmholtz Acoustic Problems,” Journal of Sound and Vibration 486, no. 2 (2020): 115598, https://doi.org/10.1016/j.jsv.2020.115598.

[9] Z. Li, Y. Xu, D. Huang, and Y. Zhao, “Two-Dimensional Elasticity Solution for Free Vibration of Simple-Supported Beams With Arbitrarily and Continuously Varying Thickness,” Archive of Applied Mechanics 90, no. 2 (2020): 275–289, https://doi.org/10.1007/s00419-019-01608-y.

[10] A. M. Shaaban, C. Anitescu, E. Atroshchenko, N. Alajlan, and T. Rabczuk, “Numerical Investigations With Extended Isogeometric Boundary Element Analysis (XiBEM) for Direct and Inverse Helmholtz Acoustic Problems,” Engineering Analysis With Boundary Elements 143, no. 2 (2022): 535–546, https://doi.org/10.1016/j.enganabound.2022.06.028.

[11] J. Mergheim, E. Kuhl, and P. Steinmann, “A Finite Element Method for the Computational Modelling of Cohesive Cracks,” International Journal for Numerical Methods in Engineering 63, no. 2 (2005): 276–289, https://doi.org/10.1002/nme.v63:2.

[12] Z. Li, D. Huang, Y. Xu, and K. Yan, “Nonlocal Steady-State Thermoelastic Analysis of Functionally Graded Materials by Using Peridynamic Differential Operator,” Applied Mathematical Modelling 93 (2021): 294–313, https://doi.org/10.1016/j.apm.2020.12.004.

[13] Z. Li, D. Huang, K. Yan, and Y. Xu, “Large Deformation Analysis of Functionally Graded Beam With Variable Cross-Section by Using Peridynamic Differential Operator,” Composite Structures 279, no. 1–2 (2022): 114788, https://doi.org/10.1016/j.compstruct.2021.114788.

[14] A. M. Shaaban, “A Review Article: Isogeometric Boundary Element Analysis in Engineering Applications,” International Journal of Hydromechatronics 5, no. 4 (2022): 366–396, https://doi.org/10.1504/IJHM.2022.127039.

[15] R. Ramprasad, R. Batra, G. Pilania, A. Mannodi-Kanakkithodi, and C. Kim, “Machine Learning in Materials Informatics: Recent Applications and Prospects,” npj Computational Materials 3, no. 1 (2017): 54.

[16] F. E. Bock, R. C. Aydin, C. J. Cyron, N. Huber, S. R. Kalidindi, and B. Klusemann, “A Review of the Application of Machine Learning and Data Mining Approaches in Continuum Materials Mechanics,” Frontiers in Materials 6 (2019): 110, https://doi.org/10.3389/fmats.2019.00110.

[17] J. Schmidt, M. R. Marques, S. Botti, and M. A. Marques, “Recent Advances and Applications of Machine Learning in Solid-State Materials Science,” npj Computational Materials 5, no. 1 (2019): 83.

[18] K. T. Butler, D. W. Davies, H. Cartwright, O. Isayev, and A. Walsh, “Machine Learning for Molecular and Materials Science,” Nature 559, no. 7715 (2018): 547–555.

[19] B. Liu, N. Vu-Bac, X. Zhuang, X. Fu, and T. Rabczuk, “Stochastic Integrated Machine Learning Based Multiscale Approach for the Prediction of the Thermal Conductivity in Carbon Nanotube Reinforced Polymeric Composites,” Composites Science and Technology 224 (2022): 109425, https://doi.org/10.1016/j.compscitech.2022.109425.

[20] E. Samaniego, C. Anitescu, S. Goswami, et al., “An Energy Approach to the Solution of Partial Differential Equations in Computational Mechanics via Machine Learning: Concepts, Implementation and Applications,” Computer Methods in Applied Mechanics and Engineering 362, no. 2 (2020): 112790, https://doi.org/10.1016/j.cma.2019.112790.

[21] H. Guo, X. Zhuang, and T. Rabczuk, “A Deep Collocation Method for the Bending Analysis of Kirchhoff Plate,” preprint, arXiv, arXiv:2102.02617, 2021.

[22] X. Zhuang, H. Guo, N. Alajlan, H. Zhu, and T. Rabczuk, “Deep Autoencoder Based Energy Method for the Bending, Vibration, and Buckling Analysis of Kirchhoff Plates With Transfer Learning,” European Journal of Mechanics-A/Solids 87, no. 1 (2021): 104225, https://doi.org/10.1016/j.euromechsol.2021.104225.

[23] Y. Xia, C. Zhang, C. Wang, et al., “Prediction of Bending Strength of Glass Fiber Reinforced Methacrylate-Based Pipeline UV-CIPP Rehabilitation Materials Based on Machine Learning,” Tunnelling and Underground Space Technology 140 (2023): 105319, https://doi.org/10.1016/j.tust.2023.105319.

[24] S. R. Kalidindi and M. De Graef, “Materials Data Science: Current Status and Future Outlook,” Annual Review of Materials Research 45, no. 1 (2015): 171–193, https://doi.org/10.1146/matsci.2015.45.issue-1.

[25] A. B. Arrieta, N. Díaz-Rodríguez, J. Del Ser, et al., “Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges Toward Responsible AI,” Information Fusion 58 (2020): 82–115.

[26] B. Liu, N. Vu-Bac, X. Zhuang, X. Fu, and T. Rabczuk, “Stochastic Full-Range Multiscale Modeling of Thermal Conductivity of Polymeric Carbon Nanotubes Composites: A Machine Learning Approach,” Composite Structures 289, no. 5582 (2022): 115393, https://doi.org/10.1016/j.compstruct.2022.115393.

[27] A. Adadi and M. Berrada, “Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI),” IEEE Access 6 (2018): 52138–52160, https://doi.org/10.1109/ACCESS.2018.2870052.

[28] B. Liu, W. Lu, T. Olofsson, X. Zhuang, and T. Rabczuk, “Stochastic Interpretable Machine Learning Based Multiscale Modeling in Thermal Conductivity of Polymeric Graphene-Enhanced Composites,” Composite Structures 327, no. 7100 (2024): 117601, https://doi.org/10.1016/j.compstruct.2023.117601.

[29] I. Thawon, P. Suttakul, R. Wanison, Y. Mona, K. Y. Tippayawong, and N. Tippayawong, “Integrating Explainable Artificial Intelligence in Machine Learning Models to Enhance the Interpretation of Elastic Behaviors in Three-Dimensional-Printed Triangular Lattice Plates,” Engineering Applications of Artificial Intelligence 144, no. 32 (2025): 110148, https://doi.org/10.1016/j.engappai.2025.110148.

[30] M. Katlav, M. E. Tabar, and K. Turk, “Ai-Guided Design Framework for Bond Behavior of Steel-Concrete in Steel Reinforced Concrete Composites: From Dataset Cleaning to Feature Engineering,” Materials Today Communications 42 (2025): 111286, https://doi.org/10.1016/j.mtcomm.2024.111286.

[31] S. Sarfarazi, R. Shamass, F. Guarracino, I. Mascolo, and M. Modano, “Exploring the Stainless-Steel Beam-To-Column Connections Response: A Hybrid Explainable Machine Learning Framework for Characterization,” Frontiers of Structural and Civil Engineering 19 (2025): 34–59.

[32] R. Dangayach, N. Jeong, E. Demirel, N. Uzal, V. Fung, and Y. Chen, “Machine Learning-Aided Inverse Design and Discovery of Novel Polymeric Materials for Membrane Separation,” Environmental Science & Technology 59, no. 2 (2024): 993–1012.

[33] Y. Gu, K. Wang, Z. Zhang, et al., “Accelerating the Design and Discovery of Tribocorrosion-Resistant Metals By Interfacing Multiphysics Modeling With Machine Learning and Genetic Algorithms,” npj Materials Degradation 9, no. 1 (2025): 7.

[34] G. j. Shi, D. y. Wang, D. w. Gao, and Q. Li, “Impact Load Identification of Ship Panels Under Tire Drop Action Based on the Data-Driven Regression With Regularization,” Ocean Engineering 322, no. 2 (2025): 120441, https://doi.org/10.1016/j.oceaneng.2025.120441.

[35] V. Daghigh, S. B. Ramezani, H. Daghigh, and T. E. Lacy,, “Explainable Artificial Intelligence Prediction of Defect Characterization in Composite Materials,” Composites Science and Technology 256, no. 13 (2024): 110759, https://doi.org/10.1016/j.compscitech.2024.110759.

[36] J. A. Guzmán-Torres, F. J. Domínguez-Mota, G. Tinoco-Guerrero, J. G. Tinoco-Ruíz, and E. M. Alonso-Guzmán, “Extreme Fine-Tuning and Explainable AI Model for Non-Destructive Prediction of Concrete Compressive Strength, the Case of Concretexai Dataset,” Advances in Engineering Software 192, no. 3 (2024): 103630, https://doi.org/10.1016/j.advengsoft.2024.103630.

[37] Q. Qasaimeh, J. Liu, D. F. Silva, A. Qasaimeh, J. L. Evans, and S. Hamasha, “Interpretable Data-Driven Framework for Prediction of Homogenous Lead-Free Solder Joints in Ball Grid Array Packages,” Journal of Intelligent Manufacturing (2025): 1–19, https://doi.org/10.1007/s10845-025-02564-x.

[38] N. Van-Canh, N. Anh-Thang, P. Ngoc-Linh, and N. Thuy-Duong, “Multi-Objective Optimization of SUS430C Steel Turning Process Using Hybrid Machine Learning and Evolutionary Algorithm Approach,” Results in Engineering 25 (2025): 104233.

[39] B. Liu, N. Vu-Bac, and T. Rabczuk, “A Stochastic Multiscale Method for the Prediction of the Thermal Conductivity of Polymer Nanocomposites Through Hybrid Machine Learning Algorithms,” Composite Structures 273, no. 1 (2021): 114269, https://doi.org/10.1016/j.compstruct.2021.114269.

[40] B. Liu and W. Lu, “Surrogate Models in Machine Learning for Computational Stochastic Multi-Scale Modelling in Composite Materials Design,” International Journal of Hydromechatronics 5, no. 4 (2022): 336–365, https://doi.org/10.1504/IJHM.2022.127037.

[41] B. Liu, N. Vu-Bac, X. Zhuang, W. Lu, X. Fu, and T. Rabczuk, “Al-Demat: A Web-Based Expert System Platform for Computationally Expensive Models in Materials Design,” Advances in Engineering Software 176, no. 7 (2023): 103398, https://doi.org/10.1016/j.advengsoft.2022.103398.

[42] K. Choudhary, B. DeCost, C. Chen, et al., “Recent Advances and Applications of Deep Learning Methods in Materials Science,” npj Computational Materials 8, no. 1 (2022): 59.

[43] L. Ward, A. Agrawal, A. Choudhary, and C. Wolverton, “A General-Purpose Machine Learning Framework for Predicting Properties of Inorganic Materials,” npj Computational Materials 2, no. 1 (2016): 1–7.

[44] M. T. Ribeiro, S. Singh, and C. Guestrin, “Why Should I Trust You?” Explaining the Predictions of Any Classifier,” Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, 135–1144.

[45] E. K. Sahin, “Assessing the Predictive Capability of Ensemble Tree Methods for Landslide Susceptibility Mapping Using Xgboost, Gradient Boosting Machine, and Random Forest,” SN Applied Sciences 2, no. 7 (2020): 1308, https://doi.org/10.1007/s42452-020-3060-1.

[46] J. Ukwaththa, S. Herath, and D. Meddage, “A Review of Machine Learning (Ml) and Explainable Artificial Intelligence (XAI) Methods in Additive Manufacturing (3D Printing),” Materials Today Communications 41, no. 9 (2024): 110294, https://doi.org/10.1016/j.mtcomm.2024.110294.

[47] A. Jain, S. P. Ong, G. Hautier, et al., “Commentary: The Materials Project: A Materials Genome Approach to Accelerating Materials Innovation,” APL Materials 1, no. 1 (2013): 011002, https://doi.org/10.1063/1.4812323.

[48] V. Tshitoyan, J. Dagdelen, L. Weston, et al., “Unsupervised Word Embeddings Capture Latent Knowledge From Materials Science Literature,” Nature 571, no. 7763 (2019): 95–98, https://doi.org/10.1038/s41586-019-1335-8.

[49] M. DeRousseau, J. Kasprzyk, and W. Srubar Iii, “Computational Design Optimization of Concrete Mixtures: A Review,” Cement and Concrete Research 109, no. 6 (2018): 42–53, https://doi.org/10.1016/j.cemconres.2018.04.007.

[50] B. W. Chong, R. Othman, R. Putra Jaya, et al., “Design of Experiment on Concrete Mechanical Properties Prediction: A Critical Review,” Materials 14, no. 8 (2021): 1866.

[51] B. Liu, S. R. Penaka, W. Lu, K. Feng, A. Rebbling, and T. Olofsson, “Data-Driven Quantitative Analysis of an Integrated Open Digital Ecosystems Platform for User-Centric Energy Retrofits: A Case Study in Northern Sweden,” Technology in Society 75 (2023): 102347, https://doi.org/10.1016/j.techsoc.2023.102347.

[52] B. Liu, N. Vu-Bac, X. Zhuang, and T. Rabczuk, “Stochastic Multiscale Modeling of Heat Conductivity of Polymeric Clay Nanocomposites,” Mechanics of Materials 142, no. 268 (2020): 103280, https://doi.org/10.1016/j.mechmat.2019.103280.

[53] D. Petcu, C. Craciun, and M. Rak, Towards a Cross Platform Cloud API,” in First International Conference on Cloud Computing and Services Science, 2011, 166–169.

[54] S. V. Kalinin, B. G. Sumpter, and R. K. Archibald, “Big-Deep-Smart Data in Imaging for Guiding Materials Design,” Nature Materials 14, no. 10 (2015): 973–980, https://doi.org/10.1038/nmat4395.

[55] S. Aggarwal, Flask Framework Cookbook: Enhance Your Flask Skills With Advanced Techniques and Build Dynamic, Responsive Web Applications (Packt Publishing Ltd, 2023).

[56] N. J. Gogtay and U. M. Thatte, “Principles of Correlation Analysis,” Journal of the Association of Physicians of India 65, no. 3 (2017): 78–81.

[57] M. O. Akinwande, H. G. Dikko, A. Samson, others, “Variance Inflation Factor: As a Condition for the Inclusion of Suppressor Variable (S) in Regression Analysis,” Open Journal of Statistics 5, no. 07 (2015): 754, https://doi.org/10.4236/ojs.2015.57075.

[58] A. Saltelli, S. Tarantola, F. Campolongo, and M. Ratto, Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models (Wiley Online Library, 2004).

[59] A. Saltelli, P. Annoni, I. Azzini, F. Campolongo, M. Ratto, and S. Tarantola, “Variance Based Sensitivity Analysis of Model Output. Design and Estimator for the Total Sensitivity Index,” Computer Physics Communications 181, no. 2 (2010): 259–270.

[60] H. Abdi and L. J. Williams, “Principal Component Analysis,” Wiley Interdisciplinary Reviews: Computational Statistics 2, no. 4 (2010): 433–459, https://doi.org/10.1002/wics.v2:4.

[61] M. I. Jordan and T. M. Mitchell, “Machine Learning: Trends, Perspectives, and Prospects,” Science 349, no. 6245 (2015): 255–260.

[62] S. Shalev-Shwartz and S. Ben-David, Understanding Machine Learning: From Theory to Algorithms (Cambridge University Press, 2014).

[63] S. Lundberg, “A Unified Approach to Interpreting Model Predictions,” preprint, arXiv, arXiv:1705.07874, 2017.

[64] H. Wu, Q. Liu, X. Liu, Y. Zhang, and Z. Yang, “An Edge-Assisted Cloud Framework Using a Residual Concatenate FCN Approach to Beam Correction in the Internet of Weather Radars,” World Wide Web 25, no. 5 (2022): 1923–1949, https://doi.org/10.1007/s11280-021-00988-y.

[65] I. C. Yeh, “Modeling of Strength of High-Performance Concrete Using Artificial Neural Networks,” Cement and Concrete Research 28, no. 12 (1998): 1797–1808, https://doi.org/10.1016/S0008-8846(98)00165-3.

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