Page 77 - Read Online

Page 18 of 19 Tang et al. J. Mater. Inf. 2025, 5, 38 https://dx.doi.org/10.20517/jmi.2025.05

19. Donoho, D. L. High-dimensional data analysis: the curses and blessings of dimensionality. In AMS Conference on Math Challenges of
the 21st Century; AMS, 2000. https://dl.icdst.org/pdfs/files/236e636d7629c1a53e6ed4cce1019b6e.pdf. (accessed 16 Jun 2025)
20. Allen, A. E. A.; Tkatchenko, A. Machine learning of material properties: predictive and interpretable multilinear models. Sci. Adv.
2022, 8, eabm7185. DOI PubMed PMC
21. Liu, D. D.; Li, Q.; Zhu, Y. J.; et al. High-throughput phase field simulation and machine learning for predicting the breakdown
performance of all-organic composites. J. Phys. D. Appl. Phys. 2024, 57, 415502. DOI
22. Rabitz, H.; Aliş, Ö. F. General foundations of high-dimensional model representations. J. Math. Chem. 1999, 25, 197-233. DOI
23. Rabitz, H.; Aliş, Ö. F.; Shorter, J.; Shim, K. Efficient input - output model representations. Comput. Phys. Commun. 1999, 117, 11-20.
DOI
24. Li, G.; Hu, J.; Wang, S. W.; Georgopoulos, P. G.; Schoendorf, J.; Rabitz, H. Random sampling-high dimensional model representation
(RS-HDMR) and orthogonality of its different order component functions. J. Phys. Chem. A. 2006, 110, 2474-85. DOI PubMed
25. Manzhos, S.; Carrington, T.; Ihara, M. Orders of coupling representations as a versatile framework for machine learning from sparse
data in high-dimensional spaces. Artif. Intell. Chem. 2023, 1, 100008. DOI
26. Ren, O.; Boussaidi, M. A.; Voytsekhovsky, D.; Ihara, M.; Manzhos, S. Random sampling high dimensional model representation
Gaussian process regression (RS-HDMR-GPR) for representing multidimensional functions with machine-learned lower-dimensional
terms allowing insight with a general method. Comput. Phys. Commun. 2022, 271, 108220. DOI
27. Li, G.; Xing, X.; Welsh, W.; Rabitz, H. High dimensional model representation constructed by support vector regression. I.
Independent variables with known probability distributions. J. Math. Chem. 2017, 55, 278-303. DOI
28. Chen, H.; Wong, L. L.; Adams, S. SoftBV - a software tool for screening the materials genome of inorganic fast ion conductors. Acta.
Crystallogr. B. Struct. Sci. Cryst. Eng. Mater. 2019, 75, 18-33. DOI PubMed
29. Wong, L. L.; Phuah, K. C.; Dai, R.; Chen, H.; Chew, W. S.; Adams, S. Bond valence pathway analyzer - an automatic rapid screening
tool for fast ion conductors within softBV. Chem. Mater. 2021, 33, 625-41. DOI
30. Li, Y.; Zhu, R.; Wang, Y.; Feng, L.; Liu, Y. Center-environment deep transfer machine learning across crystal structures: from spinel
oxides to perovskite oxides. npj. Comput. Mater. 2023, 9, 1068. DOI
31. Perepezko, J. H. Materials science. The hotter the engine, the better. Science 2009, 326, 1068-9. DOI PubMed
32. Bewlay, B. P.; Jackson, M. R.; Zhao, J.; Subramanian, P. R.; Mendiratta, M. G.; Lewandowski, J. J. Ultrahigh-temperature Nb-silicide-
based composites. MRS. Bull. 2003, 28, 646-53. DOI
33. Shu, J.; Dong, Z.; Zheng, C.; et al. High-throughput experiment-assisted study of the alloying effects on oxidation of Nb-based alloys.
Corros. Sci. 2022, 204, 110383. DOI
34. Shi, S.; Zhu, L.; Jia, L.; Zhang, H.; Sun, Z. Ab-initio study of alloying effects on structure stability and mechanical properties of α-Nb
5
Si . Comput. Mater. Sci. 2015, 108, 121-7. DOI
3
35. Xu, W.; Han, J.; Wang, C.; et al. Temperature-dependent mechanical properties of alpha-/beta-Nb Si phases from first-principles
5 3
calculations. Intermetallics 2014, 46, 72-9. DOI
36. Papadimitriou, I.; Utton, C.; Tsakiropoulos, P. The impact of Ti and temperature on the stability of Nb Si phases: a first-principles
5 3
study. Sci. Technol. Adv. Mater. 2017, 18, 467-79. DOI PubMed PMC
37. Liu, G.; Jia, L.; Kong, B.; Guan, K.; Zhang, H. Artificial neural network application to study quantitative relationship between silicide
and fracture toughness of Nb-Si alloys. Mater. Design. 2017, 129, 210-8. DOI
38. Hart, G. L. W.; Mueller, T.; Toher, C.; Curtarolo, S. Machine learning for alloys. Nat. Rev. Mater. 2021, 6, 730-55. DOI
39. Li, Y.; Xiao, B.; Tang, Y.; et al. Center-environment feature model for machine learning study of spinel oxides based on first-
principles computations. J. Phys. Chem. C. 2020, 124, 28458-68. DOI
40. Wang, X.; Xiao, B.; Li, Y.; et al. First-principles based machine learning study of oxygen evolution reactions of perovskite oxides
using a surface center-environment feature model. Appl. Surf. Sci. 2020, 531, 147323. DOI
41. Tang, Y.; Xiao, B.; Chen, J.; et al. Multi-component alloying effects on the stability and mechanical properties of Nb and Nb–Si
alloys: a first-principles study. Metall. Mater. Trans. A. 2023, 54, 450-72. DOI
42. Ouyang, R.; Curtarolo, S.; Ahmetcik, E.; Scheffler, M.; Ghiringhelli, L. M. SISSO: a compressed-sensing method for identifying the
best low-dimensional descriptor in an immensity of offered candidates. Phys. Rev. Mater. 2018, 2, 083802. DOI
43. A.A.Baikov Institute of Metallurgy and Materials Science. Database on properties of chemical elements. http://phases.imet-db.ru/
elements/mendel.aspx?main=1. (accessed 16 Jun 2025).
44. Ducker, H.; Burges, C. J. C.; Kaufman, L.; Smola, A. J.; Vapnik, V. Support vector regression machines. In Advances in Neural
I n f o r m a t i o n P r o c e s s i n g Systems. 9 9 6 . h t t p s : / / p r o c e e d i n g s . n e u r i p s . c c / p a p e r _ f i l e s / p a p e r / 1 9 9 6 / f i l e /
1
d38901788c533e8286cb6400b40b386d-Paper.pdf. (accessed 16 Jun 2025)
45. Chang, C. C.; Lin, C. J. LIBSVM: a library for support vector machines. ACM. Trans. Intell. Syst. Technol. 2011, 2, 1-27. DOI
46. Breiman, L. Random forests. Mach. Learn. 2001, 45, 5-32. DOI
47. Manzhos, S.; Ihara, M. Neural network with optimal neuron activation functions based on additive Gaussian process regression. J.
Phys. Chem. A. 2023, 127, 7823-35. DOI PubMed
48. Rasmussen, C. E.; Williams, C. K. I. Gaussian processes for machine learning. The MIT Press; 2005. https://gaussianprocess.org/gpml/
chapters/RW.pdf. (accessed 16 Jun 2025).
49. Duvenaud, D. K.; Nickisch, H.; Rasmussen, C. E. Additive Gaussian processes. In: Advances in Neural Information Processing
Systems. 2011. https://proceedings.neurips.cc/paper_files/paper/2011/file/4c5bde74a8f110656874902f07378009-Paper.pdf. (accessed