iamrp.net-RZgs-1790645728-j408231924 1790645728 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 Andrei Popescu Mihai Ionescu Elena Stan Sorin Dumitrescu Irina Pavel 2026 10.68159/j408231924 https://iamrp.net/pub/journal/2/article/j408231924 Yang K, Song Y, Li Y, Smedskjaer MM, Bauchy M, Rosner F. Enabling extrapolation of Young’s modulus of CaO-Al2O3-SiO2 ternary glasses by topology-informed machine learning. J Non Cryst Solids. 2025;666:123610. 10.1016/j.jnoncrysol.2025.123610 Muckley ES, Saal JE, Meredig B, Roper CS, Martin JH. Interpretable models for extrapolation in scientific machine learning. Digit Discov. 2023;2(5):1425-35. 10.1039/D3DD00082F 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 Pegolo P, Grasselli F. Thermal transport of glasses via machine learning driven simulations. Front Mater. 2024;11:1369034. 10.3389/fmats.2024.1369034 Sinz P, Swift MW, Brumwell X, Liu J, Kim KJ, Qi Y, et al. Wavelet scattering networks for atomistic systems with extrapolation of material properties. J Chem Phys. 2020;153(8):084109. 10.1063/5.0016020 Liu S, Bocklund B, Diffenderfer J, Chaganti S, Kailkhura B, McCall SK, et al. A comparative study of predicting high entropy alloy phase fractions with traditional machine learning and deep neural networks. npj Comput Mater. 2024;10(1):172. 10.1038/s41524-024-01335-1 Szukalo RJ, Giovambattista N, Debenedetti PG. Computational investigation of water glasses using machine-learning potentials. Proc Natl Acad Sci U S A. 2025;122(32):e2509609122. 10.1073/pnas.2509609122 Valente M, Dias TC, Guerra V, Ventura R. Physics-consistent machine learning with output projection onto physical manifolds. Commun Phys. 2025;8(1):433. 10.1038/s42005-025-02329-1 Wang X, Kan Q, Liu H, Petrů M, Kang G. Physics-informed probabilistic machine learning with knowledge distillation for cross-system prediction of elastic properties in glasses for composite design. Compos Part A Appl Sci Manuf. 2026:109734. Liu F, Chowdhury A. Deep learning with physics priors as generalized regularizers. arXiv:2312.08678 [Preprint]. 2023. Murphy KP. Probabilistic machine learning: an introduction. Cambridge (MA): MIT Press; 2022. Qi Y, Gong W, Yan Q. Bridging deep learning force fields and electronic structures with a physics-informed approach. npj Comput Mater. 2025;11(1):177. 10.1038/s41524-025-01668-5 Yazdani Sarvestani H, Nadigotti S, Fatehi E, Aranguren van Egmond D, Ashrafi B. Beyond order: Perspectives on leveraging machine learning for disordered materials. Adv Eng Mater. 2025;27(22):2402486. 10.1002/adem.202402486 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 Tenorio M, Rahman MH, Mannodi-Kanakkithodi A, Chapman J. Out-of-distribution machine learning for materials discovery: Challenges and opportunities. Chem Phys Rev. 2026;7(1):011317. 10.1063/5.0228239 Arjovsky M. Out of distribution generalization in machine learning [dissertation]. New York: New York University; 2020. Hooven NE, Lin AY, Cersonsky RK. Extrapolation of machine-learning interatomic potentials for organic and polymeric systems. arXiv:2509.25022 [Preprint]. 2025. Liu H, Zhang T, Anoop Krishnan NM, Smedskjaer MM, Ryan JV, Gin S, et al. Predicting the dissolution kinetics of silicate glasses by topology-informed machine learning. npj Mater Degrad. 2019;3(1):32. 10.1038/s41529-019-0094-1 Yao Y, Cappola J, Kethamukkala K, Gu Y, Li L. Machine learning-enhanced multiscale material mechanics modeling for complex alloys. Acc Mater Res. 2026;7(3):234-48. 10.1021/accountsmr.5c00242 Teichert GH, Natarajan AR, Van der Ven A, Garikipati K. Scale bridging materials physics: Active learning workflows and integrable deep neural networks for free energy function representations in alloys. Comput Methods Appl Mech Eng. 2020;371:113281. 10.1016/j.cma.2020.113281 Jung G, Alkemade RM, Bapst V, Coslovich D, Filion L, Landes FP, et al. Roadmap on machine learning glassy dynamics. Nat Rev Phys. 2025;7(2):91-104. 10.1038/s42254-024-00791-4 Phan AD, Que NT, Duyen NTT, Phan TV, Quach KQ, Mei B. Bridging machine learning and glassy dynamics theory for predictive polymer modeling. J Appl Phys. 2025;138(4):044703. 10.1063/5.0280443 Craig DL, Moon H, Fedele F, Lennon DT, van Straaten B, Vigneau F, et al. Bridging the reality gap in quantum devices with physics-aware machine learning. Phys Rev X. 2024;14(1):011001. 10.1103/PhysRevX.14.011001 Martin TB, Audus DJ. Emerging trends in machine learning: A polymer perspective. ACS Polym Au. 2023;3(3):239-58. 10.1021/acspolymersau.2c00053 Canatar A, Bordelon B, Pehlevan C. Out-of-distribution generalization in kernel regression. In: Advances in Neural Information Processing Systems. 2021;34:12600-12. Shu C, Zhang S, Ding P, Sun Y, Tao X, Zhu X, et al. Physics-enhanced machine learning for predicting strength of high-carbon chromium steel during thermomechanical processing and spheroidizing annealing. Mater Des. 2025;256:114333. 10.1016/j.matdes.2025.114333 Xie T, France-Lanord A, Wang Y, Lopez J, Stolberg MA, Hill M, et al. Accelerating amorphous polymer electrolyte screening by learning to reduce errors in molecular dynamics simulated properties. Nat Commun. 2022;13(1):3415. 10.1038/s41467-022-30994-1 Afflerbach BT, Francis C, Schultz LE, Spethson J, Meschke V, Strand E, et al. Machine learning prediction of the critical cooling rate for metallic glasses from expanded datasets and elemental features. Chem Mater. 2022;34(7):2945-54. 10.1021/acs.chemmater.1c03542