REFERENCES
2. Zhang X, Jiang Y. Research and application of machine learning in automatic program generation. Chin j electron 2020;29:1001-15.
3. Zhang X, Liu C, Suen CY. Towards robust pattern recognition: a review. Proc IEEE 2020;108:894-922.
4. Rani S, Lakhwani K, Kumar S. Three dimensional objects recognition & pattern recognition technique; related challenges: a review. Multimed Tools Appl 2022;81:17303-46.
5. Cipriano LE. Evaluating the impact and potential impact of machine learning on medical decision making. Med Decis Making 2023;43:147-9.
6. Zhong X, Gallagher B, Liu S, Kailkhura B, Hiszpanski A, Han TY. Explainable machine learning in materials science. npj Comput Mater 2022:8.
7. Cai J, Chu X, Xu K, Li H, Wei J. Machine learning-driven new material discovery. Nanoscale Adv 2020;2:3115-30.
9. Fu Z, Liu W, Huang C, Mei T. A review of performance prediction based on machine learning in materials science. Nanomaterials 2022;12:2957.
10. Mohtasham Moein M, Saradar A, Rahmati K, et al. Predictive models for concrete properties using machine learning and deep learning approaches: a review. J Build Eng 2023;63:105444.
11. Vivanco-benavides LE, Martínez-gonzález CL, Mercado-zúñiga C, Torres-torres C. Machine learning and materials informatics approaches in the analysis of physical properties of carbon nanotubes: a review. Comput Mater Sci 2022;201:110939.
12. Park S, Han H, Kim H, Choi S. Machine learning applications for chemical reactions. Chem Asian J 2022;17:e202200203.
13. Bartel CJ, Trewartha A, Wang Q, Dunn A, Jain A, Ceder G. A critical examination of compound stability predictions from machine-learned formation energies. npj Comput Mater 2020;6:97.
14. Yu Z, Liu Q, Szlufarska I, Wang B. Structural signatures for thermodynamic stability in vitreous silica: Insight from machine learning and molecular dynamics simulations. Phys Rev Materials 2021;5:015602.
15. Yang Z, Gao W. Applications of machine learning in alloy catalysts: rational selection and future development of descriptors. Adv Sci 2022;9:e2106043.
16. Tao Q, Xu P, Li M, Lu W. Machine learning for perovskite materials design and discovery. npj Comput Mater 2021;7:23.
17. Timkina YA, Tuchin VS, Litvin AP, Ushakova EV, Rogach AL. Ytterbium-doped lead-halide perovskite nanocrystals: synthesis, near-infrared emission, and open-source machine learning model for prediction of optical properties. Nanomaterials 2023;13:744.
18. Xu P, Chen H, Li M, Lu W. New opportunity: machine learning for polymer materials design and discovery. Advcd Theory and Sims 2022;5:2100565.
19. Martin TB, Audus DJ. Emerging trends in machine learning: a polymer perspective. ACS Polym Au 2023;3:239-58.
20. Xu P, Ji X, Li M, Lu W. Small data machine learning in materials science. npj Comput Mater 2023;9:42.
21. Swain MC, Cole JM. ChemDataExtractor: A toolkit for automated extraction of chemical information from the scientific literature. J Chem Inf Model 2016;56:1894-904.
22. Li Z, Zhang Z, Xiong B, et al. Materials science database in material research and development: recent applications and prospects. Frontiers Data Comput 2020;2:78-90.
23. Stein HS, Sanin A, Rahmanian F, et al. From materials discovery to system optimization by integrating combinatorial electrochemistry and data science. Curr Opin Electrochem 2022;35:101053.
24. Schleder GR, Padilha ACM, Acosta CM, Costa M, Fazzio A. From DFT to machine learning: recent approaches to materials science - a review. J Phys Mater 2019;2:032001.
25. Lin GSS, Tan WW, Tan HJ, Khoo CW, Afrashtehfar KI. Innovative pedagogical strategies in health professions education: active learning in dental materials science. Int J Environ Res Public Health 2023;20:2041.
26. Henderson AR. The bootstrap: a technique for data-driven statistics. Using computer-intensive analyses to explore experimental data. Clin Chim Acta 2005;359:1-26.
27. Zhu Q, Gong H, Xu Y, et al. A bootstrap based virtual sample generation method for improving the accuracy of modeling complex chemical processes using small datasets. In: IEEE 6th Data Driven Control and Learning Systems Conference; 2017 May 26-27; Chongqing, China. IEEE; 2017. p. 84-88.
28. Han P, Yao X, Zhan J, et al. A bootstrap-bayesian dynamic modification model based on small sample target features. In: Global Oceans 2020: Singapore - U.S. Gulf Coast;2020 Oct 5-30; Biloxi, MS, USA. IEEE; 2020; p. 1-6.
31. Gong H, Chen Z, Zhu Q, He Y. A Monte Carlo and PSO based virtual sample generation method for enhancing the energy prediction and energy optimization on small data problem: an empirical study of petrochemical industries. Appl Energy 2017;197:405-15.
32. Valle Y, Venayagamoorthy G, Mohagheghi S, Hernandez J, Harley R. Particle swarm optimization: basic concepts, variants and applications in power systems. IEEE Trans Evol Computat 2008;12:171-95.
33. Chen Z, Zhu B, He Y, Yu L. A PSO based virtual sample generation method for small sample sets: applications to regression datasets. Eng Appl Artif Intell 2017;59:236-43.
34. Yu L, Zhang X. Can small sample dataset be used for efficient internet loan credit risk assessment? Evidence from online peer to peer lending. Fin Res Lett 2021;38:101521.
35. Wu S, Wang B, Zhao J, Zhao M, Zhong K, Guo Y. Virtual sample generation and ensemble learning based image source identification with small training samples. Int J Digit Crime Forensics 2021;13:34-46.
36. Li D, Wu C, Tsai T, Lina Y. Using mega-trend-diffusion and artificial samples in small data set learning for early flexible manufacturing system scheduling knowledge. Comput Oper Res 2007;34:966-82.
37. Guo Z, Tang J, Qiao J. An improved virtual sample generation technology based on mega trend diffusion. In: 2019 Chinese Automation Congress (CAC); 2019 Nov 22-24; Hangzhou, China. IEEE; 2020. p. 22-24.
38. Zhu B, Yu L, Geng Z. Cost estimation method based on parallel Monte Carlo simulation and market investigation for engineering construction project. Cluster Comput 2016;19:1293-308.
39. Yu X, He Y, Xu Y, Zhu Q. A Mega-Trend-Diffusion and Monte Carlo based virtual sample generation method for small sample size problem. J Phys Conf Ser 2019;1325:012079.
40. Shen L, Qian Q. A virtual sample generation algorithm supporting machine learning with a small-sample dataset: a case study for rubber materials. Comput Mater Sci 2022;211:111475.
41. Reynolds DA, Quatieri TF, Dunn RB. Speaker verification using adapted gaussian mixture models. Digit Signal Process 2000;10:19-41.
42. Xu P, Chen C, Chen S, Lu W, Qian Q, Zeng Y. Machine learning-assisted design of yttria-stabilized zirconia thermal barrier coatings with high bonding strength. ACS Omega 2022;7:21052-61.
43. Talekar B. A Detailed review on decision tree and random forest. Biosci Biotech Res Comm 2020;13:245-8.
44. Hu J, Szymczak S. A review on longitudinal data analysis with random forest. Brief Bioinform 2023;24:bbad002.
45. He YL, Hua Q, Zhu QX, Lu S. Enhanced virtual sample generation based on manifold features: applications to developing soft sensor using small data. ISA Trans 2022;126:398-406.
46. Gui J, Sun Z, Wen Y, Tao D, Ye J. A review on generative adversarial networks: algorithms, theory, and applications. IEEE Trans Knowl Data Eng 2023;35:3313-32.
47. Cheng J, Yang Y, Tang X, et al. Generative adversarial networks: a literature review. KSII T Internet Info 2020;14:4625-4647.
48. Cui C, Tang J, Xia H, Qiao J, Yu W. Virtual sample generation method based on generative adversarial fuzzy neural network. Neural Comput Appl 2023;35:6979-7001.
49. He Y, Li X, Ma J, Lu S, Zhu Q. A novel virtual sample generation method based on a modified conditional Wasserstein GAN to address the small sample size problem in soft sensing. J Process Control 2022;113:18-28.
50. Zhu Q, Hou K, Chen Z, Gao Z, Xu Y, He Y. Novel virtual sample generation using conditional GAN for developing soft sensor with small data. Eng Appl Artif Intell 2021;106:104497.
51. Aggarwal S, Singh P. Cuckoo, Bat and Krill Herd based k-means++ clustering algorithms. Cluster Comput 2019;22:14169-80.
52. Liu Z, Guo J, Chen Z, et al. Swarm intelligence for new materials. Comput Mater Sci 2022;214:111699.
53. Yan S, Wang Y, Gao Z, Long Y, Ren J. Directional design of materials based on multi-objective optimization: a case study of two-dimensional thermoelectric SnSe. Chinese Phys Lett 2021;38:027301.
54. Shim S, Park WB, Han J, et al. Optimal composition of Li argyrodite with harmonious conductivity and chemical/electrochemical stability: fine-tuned via tandem particle swarm optimization. Adv Sci 2022;9:e2201648.
55. Zheng R, Zhang C. Optimized design of absorbing structural materials using a particle swarm optimization algorithm. Mod Def Technol 2019;47:88-93. (in Chinese) Available from: https://xueshu.baidu.com/usercenter/paper/show?paperid=1k2b08n0gs0d0ep0b03q0040ef231476&site=xueshu_se. [Last accessed on 6 Jul 2023].
56. Chen L, Liao C, Lin W, Chang L, Zhong X. Hybrid-surrogate-model-based efficient global optimization for high-dimensional antenna design. PIER 2012;124:85-100.
57. Xu B, Cai Y. A multiple-data-based efficient global optimization algorithm and its parallel implementation for automotive body design. Adv Mech Eng 2018;10:168781401879434.
58. Bhosekar A, Ierapetritou M. Advances in surrogate based modeling, feasibility analysis, and optimization: a review. Comput Chem Eng 2018;108:250-67.
59. Raponi E, Fiumarella D, Boria S, Scattina A, Belingardi G. Methodology for parameter identification on a thermoplastic composite crash absorber by the sequential response surface method and efficient global optimization. Compos Struct 2021;278:114646.
60. Zhao W, Zheng C, Xiao B, et al. Composition refinement of 6061 aluminum alloy using active machine learning model based on Bayesian optimization sampling. Acta Metall Sin 2021;57:797-810.
61. Xue D, Balachandran PV, Hogden J, Theiler J, Xue D, Lookman T. Accelerated search for materials with targeted properties by adaptive design. Nat Commun 2016;7:11241.
62. Zhang Q, Hwang Y. Sequential model-based optimization for continuous inputs with finite decision space. Technometrics 2020;62:486-98.
63. Li B, Ma JY, Hu K, et al. A method for parameter identification of distribution network equipment based on sequential model-based optimization. Int Trans Electr 2022;2022:1-12.
64. Lu T, Li H, Li M, Wang S, Lu W. Inverse design of hybrid organic-inorganic perovskites with suitable bandgaps via proactive searching progress. ACS Omega 2022;7:21583-94.
65. Katoch S, Chauhan SS, Kumar V. A review on genetic algorithm: past, present, and future. Multimed Tools Appl 2021;80:8091-126.
67. Lim Y, Park J, Lee S, Kim J. Finely tuned inverse design of metal–organic frameworks with user-desired Xe/Kr selectivity. J Mater Chem A 2021;9:21175-83.
68. Dong R, Dan Y, Li X, Hu J. Inverse design of composite metal oxide optical materials based on deep transfer learning and global optimization. Comput Mater Sci 2021;188:110166.
69. Toropov AA, Rasulev BF, Leszczynska D, Leszczynski J. Multiplicative SMILES-based optimal descriptors: QSPR modeling of fullerene C60 solubility in organic solvents. Chem Phys Lett 2008;457:332-6.
70. Wang Y, Zeng Q, Wang J, Li Y, Fang D. Inverse design of shell-based mechanical metamaterial with customized loading curves based on machine learning and genetic algorithm. Comput Methods Appl Mech Eng 2022;401:115571.
71. Maurizi M, Gao C, Berto F. Inverse design of truss lattice materials with superior buckling resistance. npj Comput Mater 2022;8:247.
72. Nigam A, Pollice R, Aspuru-Guzik A. Parallel tempered genetic algorithm guided by deep neural networks for inverse molecular design. Digit Discov 2022;1:390-404.
73. Greenhill S, Rana S, Gupta S, Vellanki P, Venkatesh S. Bayesian optimization for adaptive experimental design: a review. IEEE Access 2020;8:13937-48.
74. Jiang M, Chen YM. Survey on Bayesian optimization algorithm. Comput Eng Des 2010;31:3254-3259. Available from: https://xueshu.baidu.com/usercenter/paper/show?paperid=ce7eea962163345bf08f16cdc1a3db8b&site=xueshu_se. [Last accessed on 6 Jul 2023].
75. Wu S, Lambard G, Liu C, Yamada H, Yoshida R. iQSPR in XenonPy: a Bayesian molecular design algorithm. Mol Inform 2020;39:e1900107.
76. Wu S, Kondo Y, Kakimoto M, et al. Machine-learning-assisted discovery of polymers with high thermal conductivity using a molecular design algorithm. npj Comput Mater 2019;5:66.
77. Serrão R, Oliveira MR, Oliveira L. Theoretical derivation of interval principal component analysis. Inf Sci 2023;621:227-47.
78. Hou S, Riley C. Is uncorrelated linear discriminant analysis really a new method? Chemom Intell Lab Syst 2015;142:49-53.
79. Yang C, Ren C, Jia Y, Wang G, Li M, Lu W. A machine learning-based alloy design system to facilitate the rational design of high entropy alloys with enhanced hardness. Acta Mater 2022;222:117431.
80. Wang X, Xu P, Lu T, et al. Inverse design of ternary gold alloy materials with low resistivity. Mater Chin 2021;40:251-256. (in Chinese) Available from: https://d.wanfangdata.com.cn/periodical/zgcljz202104002. [Last accessed on 6 Jul 2023]
81. Liu Y, Zou X, Yang Z, et al. Machine learning embedded with materials domain knowledge. J Chin Ceram Soc 2022;50:863-76. Available from: http://www.jccsoc.com/Magazine/Show.aspx?ID=51304.[Last accessed on 27 Jul 2023]
