Page 103 - Read Online

Shu et al. J. Mater. Inf. 2025, 5, 36 https://dx.doi.org/10.20517/jmi.2025.13 Page 27 of 31

32. He, H.; Wang, Y.; Qi, Y.; Xu, Z.; Li, Y.; Wang, Y. From prediction to design: recent advances in machine learning for the study of
2D materials. Nano. Energy. 2023, 118, 108965. DOI
33. Chen, J.; Feng, M.; Zha, C.; Shao, C.; Zhang, L.; Wang, L. Machine learning-driven design of promising perovskites for photovoltaic
applications: a review. Surf. Interfaces. 2022, 35, 102470. DOI
34. Nematov, D.; Hojamberdiev, M. Machine learning - driven materials discovery: unlocking next-generation functional materials - a
minireview. arXiv 2025, arXiv:2503.18975. https://doi.org/10.48550/arXiv.2503.18975. (accessed 27 May 2025)
35. Li, Y.; Yang, K. High-throughput computational design of halide perovskites and beyond for optoelectronics. WIREs. Comput. Mol.
Sci. 2021, 11, e1500. DOI
36. Shen, L.; Zhou, J.; Yang, T.; Yang, M.; Feng, Y. P. High-throughput computational discovery and intelligent design of two-
dimensional functional materials for various applications. Acc. Mater. Res. 2022, 3, 572-83. DOI
37. Xu, D.; Zhang, Q.; Huo, X.; Wang, Y.; Yang, M. Advances in data-assisted high-throughput computations for material design. MGE.
Adv. 2023, 1, e11. DOI
38. Gan, Y.; Miao, N.; Lan, P.; Zhou, J.; Elliott, S. R.; Sun, Z. Robust design of high-performance optoelectronic chalcogenide crystals
from high-throughput computation. J. Am. Chem. Soc. 2022, 144, 5878-86. DOI PubMed
39. Lan, P.; Miao, N.; Gan, Y.; et al. High-throughput computational design of 2D ternary chalcogenides for sustainable energy. J. Phys.
Chem. Lett. 2023, 14, 10489-98. DOI
40. Bai, S.; Zhang, X.; Zhao, L. D. Rethinking SnSe thermoelectrics from computational materials science. Acc. Chem. Res. 2023, 56,
3065-75. DOI
41. Deng, T.; Qiu, P.; Yin, T.; et al. High-throughput strategies in the discovery of thermoelectric materials. Adv. Mater. 2024, 36,
e2311278. DOI PubMed
42. Xu, Y.; Elcoro, L.; Song, Z. D.; et al. High-throughput calculations of magnetic topological materials. Nature 2020, 586, 702-7. DOI
PubMed
43. Cao, G.; Ouyang, R.; Ghiringhelli, L. M.; et al. Artificial intelligence for high-throughput discovery of topological insulators: the
example of alloyed tetradymites. Phys. Rev. Mater. 2020, 4, 034204. DOI
44. Zhang, X.; Meng, W.; Liu, Y.; Dai, X.; Liu, G.; Kou, L. Magnetic electrides: high-throughput material screening, intriguing
properties, and applications. J. Am. Chem. Soc. 2023, 145, 5523-35. DOI
45. Miao, N.; Sun, Z. Computational design of two-dimensional magnetic materials. WIREs. Comput. Mol. Sci. 2022, 12, e1545. DOI
46. de Pablo, J. J.; Jackson, N. E.; Webb, M. A.; et al. New frontiers for the materials genome initiative. npj. Comput. Mater. 2019, 5,
173. DOI
47. de Pablo, J. J.; Jones, B.; Kovacs, C. L.; Ozolins, V.; Ramirez, A. P. The Materials Genome Initiative, the interplay of experiment,
theory and computation. Curr. Opin. Solid. State. Mater. Sci. 2014, 18, 99-117. DOI
48. Yu, Q.; Ma, N.; Leung, C.; Liu, H.; Ren, Y.; Wei, Z. AI in single-atom catalysts: a review of design and applications. J. Mater. Inf.
2025, 5, 9. DOI
49. Jordan, M. I.; Mitchell, T. M. Machine learning: trends, perspectives, and prospects. Science 2015, 349, 255-60. DOI PubMed
50. Xu, P.; Ji, X.; Li, M.; Lu, W. Small data machine learning in materials science. npj. Comput. Mater. 2023, 9, 1000. DOI
51. Butler, K. T.; Davies, D. W.; Cartwright, H.; Isayev, O.; Walsh, A. Machine learning for molecular and materials science. Nature
2018, 559, 547-55. DOI PubMed
52. Schleder, G. R.; Padilha, A. C. M.; Acosta, C. M.; Costa, M.; Fazzio, A. From DFT to machine learning: recent approaches to
materials science - a review. J. Phys. Mater. 2019, 2, 032001. DOI
53. Jacobsson, T. J.; Hultqvist, A.; García-Fernández, A.; et al. An open-access database and analysis tool for perovskite solar cells based
on the FAIR data principles. Nat. Energy. 2022, 7, 107-15. DOI
54. Mannodi-Kanakkithodi, A.; Chan, M. K. Y. Data-driven design of novel halide perovskite alloys. Energy. Environ. Sci. 2022, 15,
1930-49. DOI
55. Ma, B.; Wu, X.; Zhao, C.; et al. An interpretable machine learning strategy for pursuing high piezoelectric coefficients in (K Na )
0.5 0.5
NbO -based ceramics. npj. Comput. Mater. 2023, 9, 1187. DOI
3
56. Cheng, G.; Gong, X. G.; Yin, W. J. Crystal structure prediction by combining graph network and optimization algorithm. Nat.
Commun. 2022, 13, 1492. DOI PubMed PMC
57. Chen, C.; Zuo, Y.; Ye, W.; Li, X.; Deng, Z.; Ong, S. P. A critical review of machine learning of energy materials. Adv. Energy.
Mater. 2020, 10, 1903242. DOI
58. Groom, C. R.; Bruno, I. J.; Lightfoot, M. P.; Ward, S. C. The Cambridge Structural Database. Acta. Crystallogr. B. Struct. Sci. Cryst.
Eng. Mater. 2016, 72, 171-9. DOI PubMed PMC
59. Bergerhoff, G.; Hundt, R.; Sievers, R.; Brown, I. D. The inorganic crystal structure data base. J. Chem. Inf. Comput. Sci. 1983, 23,
66-9. DOI
60. Gražulis, S.; Daškevič, A.; Merkys, A.; et al. Crystallography Open Database (COD): an open-access collection of crystal structures
and platform for world-wide collaboration. Nucleic. Acids. Res. 2012, 40, D420-7. DOI PubMed PMC
61. Curtarolo, S.; Setyawan, W.; Wang, S.; et al. AFLOWLIB.ORG: a distributed materials properties repository from high-throughput
ab initio calculations. Comput. Mater. Sci. 2012, 58, 227-35. DOI
62. Jain, A.; Ong, S. P.; Hautier, G.; et al. Commentary: The Materials Project: a materials genome approach to accelerating materials
innovation. APL. Mater. 2013, 1, 011002. DOI