Page 104 - Read Online

Page 28 of 31 Shu et al. J. Mater. Inf. 2025, 5, 36 https://dx.doi.org/10.20517/jmi.2025.13

63. Gjerding, M. N.; Taghizadeh, A.; Rasmussen, A.; et al. Recent progress of the Computational 2D Materials Database (C2DB). 2D.
Mater. 2021, 8, 044002. DOI
64. Kirklin, S.; Saal, J. E.; Meredig, B.; et al. The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation
energies. npj. Comput. Mater. 2015, 1, BFnpjcompumats201510. DOI
65. Damewood, J.; Karaguesian, J.; Lunger, J. R.; et al. Representations of materials for machine learning. Annu. Rev. Mater. Res. 2023,
53, 399-426. DOI
66. Li, S.; Liu, Y.; Chen, D.; Jiang, Y.; Nie, Z.; Pan, F. Encoding the atomic structure for machine learning in materials science. WIREs.
Comput. Mol. Sci. 2022, 12, e1558. DOI
67. Oh, S. H. V.; Hwang, W.; Kim, K.; Lee, J. H.; Soon, A. Using feature-assisted machine learning algorithms to boost polarity in lead-
free multicomponent niobate alloys for high-performance ferroelectrics. Adv. Sci. 2022, 9, e2104569. DOI PubMed PMC
68. Schmidt, J.; Marques, M. R. G.; Botti, S.; Marques, M. A. L. Recent advances and applications of machine learning in solid-state
materials science. npj. Comput. Mater. 2019, 5, 221. DOI
69. Li, J.; Cheng, K.; Wang, S.; et al. Feature selection: a data perspective. ACM. Comput. Surv. 2018, 50, 1-45. DOI
70. Hsu, H.; Hsieh, C.; Lu, M. Hybrid feature selection by combining filters and wrappers. Expert. Syst. Appl. 2011, 38, 8144-50. DOI
71. Rodriguez-Galiano, V. F.; Luque-Espinar, J. A.; Chica-Olmo, M.; Mendes, M. P. Feature selection approaches for predictive
modelling of groundwater nitrate pollution: an evaluation of filters, embedded and wrapper methods. Sci. Total. Environ. 2018, 624,
661-72. DOI PubMed
72. Liu, H.; Zhou, M.; Liu, Q. An embedded feature selection method for imbalanced data classification. IEEE/CAA. J. Autom. Sinica.
2019, 6, 703-15. DOI
73. Zhang, Z.; Liu, S.; Xiong, Q.; Liu, Y. Strategic integration of machine learning in the design of excellent hybrid perovskite solar
cells. J. Phys. Chem. Lett. 2025, 16, 738-46. DOI
74. Gladkikh, V.; Kim, D. Y.; Hajibabaei, A.; Jana, A.; Myung, C. W.; Kim, K. S. Machine learning for predicting the band gaps of
ABX perovskites from elemental properties. J. Phys. Chem. C. 2020, 124, 8905-18. DOI
3
75. Gou, F.; Ma, Z.; Yang, Q.; et al. Machine learning-assisted prediction and control of bandgap for organic-inorganic metal halide
perovskites. ACS. Appl. Mater. Interfaces. 2025, 17, 18383-93. DOI PubMed
76. Wang, A. Y.; Murdock, R. J.; Kauwe, S. K.; et al. Machine learning for materials scientists: an introductory guide toward best
practices. Chem. Mater. 2020, 32, 4954-65. DOI
77. Wei, J.; Chu, X.; Sun, X.; et al. Machine learning in materials science. InfoMat 2019, 1, 338-58. DOI
78. Orupattur, N. V.; Mushrif, S. H.; Prasad, V. Catalytic materials and chemistry development using a synergistic combination of
machine learning and ab initio methods. Comput. Mater. Sci. 2020, 174, 109474. DOI
79. Ali, Y.; Awwad, E.; Al-Razgan, M.; Maarouf, A. Hyperparameter search for machine learning algorithms for optimizing the
computational complexity. Processes 2023, 11, 349. DOI
80. Cherkassky, V.; Ma, Y. Practical selection of SVM parameters and noise estimation for SVM regression. Neural. Netw. 2004, 17,
113-26. DOI PubMed
81. Cover, T.; Hart, P. Nearest neighbor pattern classification. IEEE. Trans. Inform. Theory. 1967, 13, 21-7. DOI
82. Yang, F. J. An implementation of Naive Bayes Classifier. In 2018 International Conference on Computational Science and
Computational Intelligence (CSCI), Las Vegas, USA. Dec 12-14, 2018. IEEE; 2018; pp. 301-6. DOI
83. Liu, Y.; Zhou, Q.; Cui, G. Machine learning boosting the development of advanced lithium batteries. Small. Methods. 2021, 5,
e2100442. DOI
84. Dong, X.; Yu, Z.; Cao, W.; Shi, Y.; Ma, Q. A survey on ensemble learning. Front. Comput. Sci. 2020, 14, 241-58. DOI
85. Sagi, O.; Rokach, L. Ensemble learning: a survey. WIREs. Data. Min. Knowl. 2018, 8, e1249. DOI
86. Friedman, J. H. Greedy function approximation: a gradient boosting machine. Ann. Stat. 2001, 29, 1189-232. http://www.jstor.org/
stable/2699986. (accessed 27 May 2025)
87. Freund, Y.; Schapire, R. E. Experiments with a new boosting algorithm. In Proceedings of the Thirteenth International Conference,
1996. pp. 148-56. http://www.jstor.org/stable/2699986. (accessed 27 May 2025)
88. Chen, T.; Guestrin, C. XGBoost: a scalable tree boosting system. In Proceedings of the Proceedings of the 22nd ACM SIGKDD
International Conference on Knowledge Discovery and Data Mining, San Francisco, USA. 2016; pp. 785-94. DOI
89. Pavlyshenko, B. Using stacking approaches for machine learning models. In 2018 IEEE Second International Conference on Data
Stream Mining & Processing (DSMP), Lviv, Ukraine, Aug 21-25, 2018. IEEE; 2018. pp. 255-8. DOI
90. LeCun, Y.; Bengio, Y.; Hinton, G. Deep learning. Nature 2015, 521, 436-44. DOI PubMed
91. Liu, J.; Liang, L.; Su, B.; et al. Transformative strategies in photocatalyst design: merging computational methods and deep learning.
J. Mater. Inf. 2024, 4, 33. DOI
92. Choudhary, K.; Decost, B.; Chen, C.; et al. Recent advances and applications of deep learning methods in materials science. npj.
Comput. Mater. 2022, 8, 734. DOI
93. Jain, A. K.; Mao, J.; Mohiuddin, K. M. Artificial neural networks: a tutorial. Computer 1996, 29, 31-44. DOI
94. Shelhamer, E.; Long, J.; Darrell, T. Fully convolutional networks for semantic segmentation. IEEE. Trans. Pattern. Anal. Mach.
Intell. 2017, 39, 640-51. DOI PubMed
95. Zhang, H.; Wang, Z.; Liu, D. A comprehensive review of stability analysis of continuous-time recurrent neural networks. IEEE.
Trans. Neural. Netw. Learning. Syst. 2014, 25, 1229-62. DOI