iamrp.net-L54o-1790651231-m097446732 1790651231 Institute for Advanced Materials Research Press info@iamrp.net Institute for Advanced Materials Research Press Journal of Computational and Data-Driven Materials Engineering J. Comput. Data-Driven Mater. Eng. 3149-9368 2026 5 1 Rashid Al-Mahdi Khalifa Al-Suwaidi Mariam Al-Kuwari 2026 10.68159/m097446732 https://iamrp.net/pub/journal/2/article/m097446732 Tao JL, Wang YJ. An artificial intelligence constitutive model for amorphous solids utilizing graph neural networks. JOM. 2024;76(10):5777-84. 10.1007/s11837-024-06742-9 Chapman J, Hsu T, Chen X, Heo TW, Wood BC. Quantifying disorder one atom at a time using an interpretable graph neural network paradigm. Nat Commun. 2023;14(1):4030. 10.1038/s41467-023-39755-0 Hu YJ, Zhao G, Zhang M, Bin B, Del Rose T, Zhao Q, et al. Predicting densities and elastic moduli of SiO2-based glasses by machine learning. NPJ Comput Mater. 2020;6(1):25. 10.1038/s41524-020-0291-z Li Z, Li X, Zhang H, Huang D, Zhang L. The prediction of contact force networks in granular materials based on graph neural networks. J Chem Phys. 2023;158(5):054905. 10.1063/5.0122695 Ishiai S, Yasuda I, Endo K, Yasuoka K. Graph-neural-network-based unsupervised learning of the temporal similarity of structural features observed in molecular dynamics simulations. J Chem Theory Comput. 2024;20(2):819-31. 10.1021/acs.jctc.3c00995 Yoshikawa K, Yano K, Goto S, Kim K, Matubayasi N. Graph neural network-based structural classification of glass-forming liquids and its interpretation via self-attention mechanism. J Chem Phys. 2025;163(2):024508. 10.1063/5.0277279 Zhao R, Li M, Ning Z, Sun J, Huang Y. Graph neural network-driven inverse design of relaxation dynamics in metallic-glass formers. J Non-Cryst Solids. 2025;668:123773. 10.1016/j.jnoncrysol.2025.123773 Liu H, Fu Z, Yang K, Xu X, Bauchy M. Machine learning for glass science and engineering: A review. J Non-Cryst Solids. 2021;557:119419. 10.1016/j.jnoncrysol.2019.04.039 Zhang Y, Xu X. Machine learning glass transition temperature of polyacrylamides using quantum chemical descriptors. Polym Chem. 2021;12(6):843-51. 10.1039/D0PY01581D Zhang Y, Xu X. Machine learning glass transition temperature of polymers. Heliyon. 2020;6(10):e05055. 10.1016/j.heliyon.2020.e05055 Sun YT, Bai HY, Li MZ, Wang WH. Machine learning approach for prediction and understanding of glass-forming ability. J Phys Chem Lett. 2017;8(14):3434-9. 10.1021/acs.jpclett.7b01046 Bishnoi S, Singh S, Ravinder R, Bauchy M, Gosvami NN, Kodamana H, et al. Predicting Young's modulus of oxide glasses with sparse datasets using machine learning. J Non-Cryst Solids. 2019;524:119643. 10.1016/j.jnoncrysol.2019.119643 Tah I, Ridout SA, Liu AJ. Fragility in glassy liquids: A structural approach based on machine learning. J Chem Phys. 2022;157(12):124501. 10.1063/5.0099071 Yang ZY, Wei D, Zaccone A, Wang YJ. Machine-learning integrated glassy defect from an intricate configurational-thermodynamic-dynamic space. Phys Rev B. 2021;104(6):064108. 10.1103/PhysRevB.104.064108 Liu H, Smedskjaer MM, Bauchy M. Deciphering a structural signature of glass dynamics by machine learning. Phys Rev B. 2022;106(21):214206. 10.1103/PhysRevB.106.214206 Wu JQ, Zhang HP, He YF, Li MZ. Unsupervised machine learning study on structural signature of glass transition in metallic glass-forming liquids. Acta Mater. 2023;245:118608. 10.1016/j.actamat.2022.118608 Ciarella S, Khomenko D, Berthier L, Mocanu FC, Reichman DR, Scalliet C, et al. Finding defects in glasses through machine learning. Nat Commun. 2023;14(1):4229. 10.1038/s41467-023-39948-7 Bødker ML, Bauchy M, Du T, Mauro JC, Smedskjaer MM. Predicting glass structure by physics-informed machine learning. NPJ Comput Mater. 2022;8(1):192. 10.1038/s41524-022-00882-9 Chen R, Bauchy M, Wang W, Sun Y, Tao X, Marian J. Using graph neural network and symbolic regression to model disordered systems. Sci Rep. 2025;15(1):22122. 10.1038/s41598-025-05205-8 Tao L, Varshney V, Li Y. Benchmarking machine learning models for polymer informatics: An example of glass transition temperature. J Chem Inf Model. 2021;61(11):5395-413. 10.1021/acs.jcim.1c01031 Fang Z, Yan Q. Towards accurate prediction of configurational disorder properties in materials using graph neural networks. NPJ Comput Mater. 2024;10(1):91. 10.1038/s41524-024-01283-w Fang Z, Hsu TW, Yan Q. A machine learning framework for modeling ensemble properties of atomically disordered materials. ACS Nano. 2025;19(42):37353-63. 10.1021/acsnano.5c13080 Banik S, Dhabal D, Chan H, Manna S, Cherukara M, Molinero V, et al. CEGANN: Crystal edge graph attention neural network for multiscale classification of materials environment. NPJ Comput Mater. 2023;9(1):23. 10.1038/s41524-023-00975-z Karapiperis K, Kochmann DM. Prediction and control of fracture paths in disordered architected materials using graph neural networks. Commun Eng. 2023;2(1):32. 10.1038/s44172-023-00085-0 Shiba H, Hanai M, Suzumura T, Shimokawabe T. BOTAN: BOnd TArgeting Network for prediction of slow glassy dynamics by machine learning relative motion. J Chem Phys. 2023;158(8):084503. 10.1063/5.0129791 Xiong J, Zhang TY, Shi SQ. Machine learning prediction of elastic properties and glass-forming ability of bulk metallic glasses. MRS Commun. 2019;9(2):576-85. 10.1557/mrc.2019.44 Deng B. Machine learning on density and elastic property of oxide glasses driven by large dataset. J Non-Cryst Solids. 2020;529:119768. 10.1016/j.jnoncrysol.2019.119768 Yang S, Yang Y, Huang C, Bai R, Cao L, Ren J, et al. Physics-informed graph neural networks for predicting deformation in disordered fibrous materials. Nano Lett. 2025;26(1):205-13. 10.1021/acs.nanolett.5c04895 Wang Q, Zhang L. Inverse design of glass structure with deep graph neural networks. Nat Commun. 2021;12(1):5359. 10.1038/s41467-021-25490-x Reiser P, Neubert M, Eberhard A, Torresi L, Zhou C, Shao C, et al. Graph neural networks for materials science and chemistry. Commun Mater. 2022;3(1):93. 10.1038/s43246-022-00315-6 Bapst V, Keck T, Grabska-Barwińska A, Donner C, Cubuk ED, Schoenholz SS, et al. Unveiling the predictive power of static structure in glassy systems. Nat Phys. 2020;16(4):448-54. 10.1038/s41567-020-0842-8 Liu H, Fu Z, Li Y, Sabri NF, Bauchy M. Parameterization of empirical forcefields for glassy silica using machine learning. MRS Commun. 2019;9(2):593-9. 10.1557/mrc.2019.47 Alcobaca E, Mastelini SM, Botari T, Pimentel BA, Cassar DR, de Leon Ferreira AC, et al. Explainable machine learning algorithms for predicting glass transition temperatures. Acta Mater. 2020;188:92-100. 10.1016/j.actamat.2020.01.047 Ward L, O'Keeffe SC, Stevick J, Jelbert GR, Aykol M, Wolverton C. A machine learning approach for engineering bulk metallic glass alloys. Acta Mater. 2018;159:102-11. 10.1016/j.actamat.2018.08.002