Page 59 - Read Online
P. 59
Page 18 of 18 Li et al. J. Mater. Inf. 2025, 5, 29 https://dx.doi.org/10.20517/jmi.2024.103
2021, 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. DOI
40. He, M.; Zhang, L. Machine learning and symbolic regression investigation on stability of MXene materials. Comput. Mater. Sci. 2021,
196, 110578. DOI
41. Abdusalamov, R.; Hillgärtner, M.; Itskov, M. Automatic generation of interpretable hyperelastic material models by symbolic
regression. Numer. Methods. Eng. 2023, 124, 2093-104. DOI
42. Wang, S.; Jiang, J. Interpretable catalysis models using machine learning with spectroscopic descriptors. ACS. Catal. 2023, 13, 7428-
36. DOI
43. Matsubara, Y.; Chiba, N.; Igarashi, R.; Ushiku, Y. Rethinking symbolic regression datasets and benchmarks for scientific discovery.
arXiv 2022, arXiv:2206.10540. Available online: https://doi.org/10.48550/arXiv.2206.10540. (accessed on 21 Mar 2025)
