[1] [1] YEH J W, CHEN S K, LIN S J, et al. Nanostructured high-entropy alloys with multiple principal elements: novel alloy design concepts and outcomes[J]. Adv Eng Mater, 2004, 6(5): 299–303.

[2] [2] DAI J H. Structure and toughening mechanical properties of multi principal component high entropy alloy[J]. Therm Sci, 2021, 25(6 Part A): 4019–4025.

[4] [4] CHEN R, CAI Z B, PU J B, et al. Effects of nitriding on the microstructure and properties of VAlTiCrMo high-entropy alloy coatings by sputtering technique[J]. J Alloys Compd, 2020, 827: 153836.

[5] [5] XING Q W, XIA S Q, YAN X H, et al. Mechanical properties and thermal stability of (NbTiAlSiZr)Nx high-entropy ceramic films at high temperatures[J]. J Mater Res, 2018, 33(19): 3347–3354.

[6] [6] XIANG H M, XING Y, DAI F Z, et al. High-entropy ceramics: present status, challenges, and a look forward[J]. J Adv Ceram, 2021, 10(3): 385–441.

[8] [8] RAMPRASAD R, BATRA R, PILANIA G, et al. Machine learning in materials informatics: recent applications and prospects[J]. NPJ Comput Mater, 2017, 3: 54.

[9] [9] YUE D, FENG Y, LIU X X, et al. Prediction of energy storage performance in polymer composites using high-throughput stochastic breakdown simulation and machine learning[J]. Adv Sci, 2022, 9(17): e2105773.

[10] [10] LI W X, YANG T N, LIU C S, et al. Optimizing piezoelectric nanocomposites by high-throughput phase-field simulation and machine learning[J]. Adv Sci, 2022, 9(13): e2105550.

[11] [11] JIN Y B, HE L S, WEN Z H, et al. Intelligent on-demand design of phononic metamaterials[J]. Nanophotonics, 2022, 11(3): 439–460.

[12] [12] ZHAO C H, CHUNG C C, JIANG S Y, et al. Machine-learning for designing nanoarchitectured materials by dealloying[J]. Commun Mater, 2022, 3: 86.

[13] [13] RACCUGLIA P, ELBERT K C, ADLER P D F, et al. Machine-learning-assisted materials discovery using failed experiments[J]. Nature, 2016, 533(7601): 73–76.

[14] [14] KATUBI K M, SAQIB M, MARYAM M, et al. Machine learning assisted designing of organic semiconductors for organic solar cells: high-throughput screening and reorganization energy prediction[J]. Inorg Chem Commun, 2023, 151: 110610.

[16] [16] ZHANG J, XIANG X P, XU B, et al. Rational design of high-entropy ceramics based on machine learning – A critical review[J]. Curr Opin Solid State Mater Sci, 2023, 27(2): 101057.

[17] [17] SARKER P, HARRINGTON T, TOHER C, et al. High-entropy high-hardness metal carbides discovered by entropy descriptors[J]. Nat Commun, 2018, 9(1): 4980.

[18] [18] KAUFMANN K, MARYANOVSKY D, MELLOR W M, et al. Discovery of high-entropy ceramics via machine learning[J]. NPJ Comput Mater, 2020, 6: 42.

[19] [19] HUANG H Y, SHAO L H, LIU H Z. Prediction of single-phase high-entropy nitrides from first-principles calculations[J]. Phys Status Solidi B, 2021, 258(8): 2100140.

[20] [20] WARD L, AGRAWAL A, CHOUDHARY A, et al. A general-purpose machine learning framework for predicting properties of inorganic materials[J]. NPJ Comput Mater, 2016, 2: 16028.

[21] [21] LEWIN E. Multi-component and high-entropy nitride coatings—a promising field in need of a novel approach[J]. J Appl Phys, 2020, 127(16): 160901.