REFERENCES

1. Hao, J.; Wilkie, C. A.; Wang, J. An XPS investigation of thermal degradation and charring of cross-linked polyisoprene and polychloroprene. Polym. Degrad. Stab. 2001, 71, 305-15.

2. Wang, J.; Du, J.; Zhu, J.; Wilkie, C. A. An XPS study of the thermal degradation and flame retardant mechanism of polystyrene-clay nanocomposites. Polym. Degrad. Stab. 2002, 77, 249-52.

3. Moo-Tun, N. M.; Valadez-González, A.; Uribe-Calderon, J. A. Thermo-oxidative aging of low density polyethylene blown films in presence of cellulose nanocrystals and a pro-oxidant additive. Polym. Bull. 2018, 75, 3149-69.

4. Cai, G.; Zhang, D.; Jiang, D.; Dong, Z. Degradation of fluorinated polyurethane coating under UVA and salt spray. Part II: Molecular structures and depth profile. Prog. Org. Coat. 2018, 124, 25-32.

5. Goliszek, M.; Podkościelna, B.; Sevastyanova, O.; Fila, K.; Chabros, A.; Pączkowski, P. Investigation of accelerated aging of lignin-containing polymer materials. Int. J. Biol. Macromol. 2019, 123, 910-22.

6. Zuo, P.; Tcharkhtchi, A.; Shirinbayan, M.; Fitoussi, J.; Bakir, F. Multiscale physicochemical characterization of a short glass fiber–reinforced polyphenylene sulfide composite under aging and its thermo-oxidative mechanism. Polym. Adv. Technol. 2019, 30, 584-97.

7. Tripathy, S. P.; Mishra, R.; Dwivedi, K. K.; Khathing, D. T.; Ghosh, S.; Fink, D. Degradation in polytetrafluoro ethylene by 62 mev protons. Radiat. Eff. Defects. Solids. 2002, 157, 303-10.

8. Khanna, N. D.; Kaur, I.; Bhalla, T. C.; Gautam, N. Effect of biodegradation on thermal and crystalline behavior of polypropylene–gelatin based copolymers. J. Appl. Polym. Sci. 2010, 118, 1476-88.

9. Nouh, S. A.; Mohamed, A.; El Hussieny, H. M.; Hegazy, T. M. Modification induced by alpha particle irradiationin Makrofol polycarbonate. J. Appl. Polym. Sci. 2008, 109, 3447-51.

10. Lei, Z.; Bliesner, S. E.; Mattson, C. N.; et al. Aerosol acidity sensing via polymer degradation. Anal. Chem. 2020, 92, 6502-11.

11. Gu, X.; Raghavan, D.; Nguyen, T.; Vanlandingham, M.; Yebassa, D. Characterization of polyester degradation using tapping mode atomic force microscopy: exposure to alkaline solution at room temperature. Polym. Degrad. Stab. 2001, 74, 139-49.

12. Park, B.; Jeong, H. Effects of acid hydrolysis on microstructure of cured urea-formaldehyde resins using atomic force microscopy. J. Appl. Polym. Sci. 2011, 122, 3255-62.

13. Mouaci, S.; Saidi, M.; Saidi-Amroun, N. Oxidative degradation and morphological properties of gamma-irradiated isotactic polypropylene films. Micro. Nano. Lett. 2017, 12, 478-81.

14. Moudoud, M.; Hedir, A.; Lamrous, O.; Diaham, S.; Touam, T. Physical ageing of insulating polystyrene from dielectric properties measurements and structural analysis. Mater. Res. Express. 2019, 6, 095324.

15. Saba, N.; Jawaid, M.; Alothman, O. Y.; Paridah, M. A review on dynamic mechanical properties of natural fibre reinforced polymer composites. Constr. Build. Mater. 2016, 106, 149-59.

16. Hu, X.; Luo, W.; Liu, X.; Li, M.; Huang, Y.; Bu, J. Temperature and frequency dependent rheological behaviour of carbon black filled natural rubber. Plast. Rubber. Compos. 2013, 42, 416-20.

17. Al-itry, R.; Lamnawar, K.; Maazouz, A. Improvement of thermal stability, rheological and mechanical properties of PLA, PBAT and their blends by reactive extrusion with functionalized epoxy. Polym. Degrad. Stab. 2012, 97, 1898-914.

18. Shimamura, H.; Nakamura, T. Mechanical properties degradation of polyimide films irradiated by atomic oxygen. Polym. Degrad. Stab. 2009, 94, 1389-96.

19. Starkova, O.; Gagani, A. I.; Karl, C. W.; Rocha, I. B. C. M.; Burlakovs, J.; Krauklis, A. E. Modelling of environmental ageing of polymers and polymer composites-durability prediction methods. Polymers 2022, 14, 907.

20. Plota, A.; Masek, A. Lifetime prediction methods for degradable polymeric materials - a short review. Materials 2020, 13, 4507.

21. Zhang, X.; Wu, Y.; Chen, X.; Wen, H.; Xiao, S. Theoretical study on decomposition mechanism of insulating epoxy resin cured by anhydride. Polymers 2017, 9, 341.

22. Ismail, A. E.; Pierce, F.; Grest, G. S. Diffusion of small penetrant molecules in polybutadienes. Mol. Phys. 2011, 109, 2025-33.

23. Vuković, F.; Walsh, T. R. Moisture ingress at the molecular scale in hygrothermal aging of fiber-epoxy interfaces. ACS. Appl. Mater. Interfaces. 2020, 12, 55278-89.

24. Meng, X.; Ye, Y.; Yang, R. Computational and experimental study on the mechanism of CO2 production during photo-oxidative degradation of poly(butylene adipate-co-terephthalate): differences between PBA and PBT segments. Macromolecules 2023, 56, 7749-62.

25. Doblies, A.; Boll, B.; Fiedler, B. Prediction of thermal exposure and mechanical behavior of epoxy resin using artificial neural networks and fourier transform infrared spectroscopy. Polymers 2019, 11, 363.

26. Yuan, W.; Hibi, Y.; Tamura, R.; et al. Revealing factors influencing polymer degradation with rank-based machine learning. Patterns 2023, 4, 100846.

27. Larché, J.; Bussière, P.; Thérias, S.; Gardette, J. Photooxidation of polymers: relating material properties to chemical changes. Polym. Degrad. Stab. 2012, 97, 25-34.

28. Li, X.; Zhao, X.; Ye, L. Stress photo-oxidative aging behaviour of polyamide 6. Polym. Int. 2012, 61, 118-23.

29. Pourmand, P.; Hedenqvist, M.; Furó, I.; Gedde, U. Deterioration of highly filled EPDM rubber by thermal ageing in air: kinetics and non-destructive monitoring. Polym. Test. 2017, 64, 267-76.

30. Neffe, A. T.; Tronci, G.; Alteheld, A.; Lendlein, A. Controlled change of mechanical properties during hydrolytic degradation of polyester urethane networks. Macromol. Chem. Phys. 2010, 211, 182-94.

31. Kaiser, E.; Kutz, J. N.; Brunton, S. L. Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proc. Math. Phys. Eng. Sci. 2018, 474, 20180335.

32. Schmidt, M.; Lipson, H. Distilling free-form natural laws from experimental data. Science 2009, 324, 81-5.

33. Kamienny, P. A.; d’Ascoli, S.; Lample, G.; Charton, F. End-to-end symbolic regression with transformers. arXiv2022, arXiv:2204.10532. Available online: https://doi.org/10.48550/arXiv.2204.10532. (accessed on 21 Mar 2025)

34. Valipour, M.; You, B.; Panju, M.; Ghodsi, A. SymbolicGPT: a generative transformer model for symbolic regression. arXiv2021, arXiv:2106.14131. Available online: https://doi.org/10.48550/arXiv.2106.14131. (accessed on 21 Mar 2025)

35. Udrescu, S. M.; Tegmark, M. AI Feynman: a physics-inspired method for symbolic regression. Sci. Adv. 2020, 6, eaay2631.

36. Petersen, B. K.; Landajuela, M.; Mundhenk, T. N.; Santiago, C. P.; Kim, S. K.; Kim, J. T. Deep symbolic regression: recovering mathematical expressions from data via risk-seeking policy gradients. arXiv2019, arXiv:1912.04871. Available online: https://doi.org/10.48550/arXiv.1912.04871. (accessed on 21 Mar 2025)

37. Landajuela, M.; Lee, C. S.; Yang, J.; et al. A unified framework for deep symbolic regression. In: Proceedings of the 36nd International Conference on Neural Information Processing Systems, New Orleans, United States of America. 2022. Available from: https://proceedings.neurips.cc/paper_files/paper/2022/file/dbca58f35bddc6e4003b2dd80e42f838-Paper-Conference.pdf. (accessed on 2025-03-21)

38. La Cava, W.; Orzechowski, P.; Burlacu, B.; et al. Contemporary symbolic regression methods and their relative performance. arXiv2021, arXiv:2107.14351. Available online: https://doi.org/10.48550/arXiv.2107.14351. (accessed on 21 Mar 2025)

39. Wang, Y.; Wagner, N.; Rondinelli, J. M. Symbolic regression in materials science. MRS. Commun. 2019, 9, 793-805.

40. He, M.; Zhang, L. Machine learning and symbolic regression investigation on stability of MXene materials. Comput. Mater. Sci. 2021, 196, 110578.

41. Abdusalamov, R.; Hillgärtner, M.; Itskov, M. Automatic generation of interpretable hyperelastic material models by symbolic regression. Numer. Methods. Eng. 2023, 124, 2093-104.

42. Wang, S.; Jiang, J. Interpretable catalysis models using machine learning with spectroscopic descriptors. ACS. Catal. 2023, 13, 7428-36.

43. Matsubara, Y.; Chiba, N.; Igarashi, R.; Ushiku, Y. Rethinking symbolic regression datasets and benchmarks for scientific discovery. arXiv2022, arXiv:2206.10540. Available online: https://doi.org/10.48550/arXiv.2206.10540. (accessed on 21 Mar 2025)

Journal of Materials Informatics
ISSN 2770-372X (Online)
Follow Us

Portico

All published articles are preserved here permanently:

https://www.portico.org/publishers/oae/

Portico

All published articles are preserved here permanently:

https://www.portico.org/publishers/oae/