journal article·2025-12

Phase structure prediction of high-entropy carbide ceramics based on two-stage data enhancement

Huaizhi Zhang, Wenwen Lin, Lu Ren, Wei Xiang, Yuejun Zhang, Lizhong Zhan

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OpenAlex topics: High Entropy Alloys Studies · Advanced materials and composites · Nuclear Materials and Properties

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References (32)

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1. Gild, J., Zhang, Y., Harrington, T., Jiang, S., Hu, T., Quinn, M.C., Mellor, W.M., Zhou, N., Vecchio, K., Luo, J.: High-entropy metal diborides: a new class of high-entropy materials and a new type of ultrahigh temperature ceramics. Sci. Rep. (2016). https://doi.org/10.1038/srep3794610.1038/srep37946 ↗
2. Zhou, J., Zhang, J., Zhang, F., Niu, B., Lei, L., Wang, W.: High-entropy carbide: a novel class of multicomponent ceramics. Ceram. Int. (2018). https://doi.org/10.1016/j.ceramint.2018.08.10010.1016/j.ceramint.2018.08.100 ↗
3. Sarker, P., Harrington, T., Toher, C., Oses, C., Samiee, M., Maria, J.-P., Brenner, D.W., Vecchio, K.S., Curtarolo, S.: High-entropy high-hardness metal carbides discovered by entropy descriptors. Nat. Commun. (2018). https://doi.org/10.1038/s41467-018-07160-710.1038/s41467-018-07160-7 ↗
4. Jin, T., Sang, X., Unocic, R.R., Kinch, R.T., Liu, X., Hu, J., Liu, H., Dai, S.: Mechanochemical-assisted synthesis of high-entropy metal nitride via a soft urea strategy. Adv. Mater. (2018). https://doi.org/10.1002/adma.20170751210.1002/adma.201707512 ↗
5. Zhang, R.-Z., Gucci, F., Zhu, H., Chen, K., Reece, M.J.: Data-driven design of ecofriendly thermoelectric high-entropy sulfides. Inorg. Chem. (2018). https://doi.org/10.1021/acs.inorgchem.8b0237910.1021/acs.inorgchem.8b02379 ↗
6. Edalati, P., Shen, X.-F., Watanabe, M., Ishihara, T., Arita, M., Fuji, M., Edalati, K.: High-entropy oxynitride as a low-bandgap and stable photocatalyst for hydrogen production. J. Mater. Chem. A (2021). https://doi.org/10.1039/D1TA03861C10.1039/D1TA03861C ↗
7. Dippo, O.F., Mesgarzadeh, N., Harrington, T.J., Schrader, G.D., Vecchio, K.S.: Bulk high-entropy nitrides and carbonitrides. Sci. Rep. (2020). https://doi.org/10.1038/s41598-020-78175-810.1038/s41598-020-78175-8 ↗
8. Oses, C., Toher, C., Curtarolo, S.: High-entropy ceramics. Nat. Rev. Mater. (2020). https://doi.org/10.1038/s41578-019-0170-810.1038/s41578-019-0170-8 ↗
9. Wang, F., Yan, X., Wang, T., Wu, Y., Shao, L., Nastasi, M., Lu, Y., Cui, B.: Irradiation damage in (Zr0.25Ta0.25Nb0.25Ti0.25)C high-entropy carbide ceramics. Acta Mater. (2020). https://doi.org/10.1016/j.actamat.2020.06.01110.1016/j.actamat.2020.06.011 ↗
10. Xiang, H., Xing, Y., Dai, F., Wang, H., Su, L., Miao, L., Zhang, G., Wang, Y., Qi, X., Yao, L., Wang, H., Zhao, B., Li, J., Zhou, Y.: High-entropy ceramics: present status, challenges, and a look forward. J. Adv. Ceram. (2021). https://doi.org/10.1007/s40145-021-0477-y10.1007/s40145-021-0477-y ↗
11. Lin, Y., Luo, N., Chamas, M., Hu, C., Grasso, S.: Sustainable high-entropy ceramics for reversible energy storage: a short review. Int. J. Appl. Ceram. Tech. (2021). https://doi.org/10.1111/ijac.1376210.1111/ijac.13762 ↗
12. Dube, T.C., Zhang, J.: Underpinning the relationship between synthesis and properties of high entropy ceramics: a comprehensive review on borides, carbides and oxides. J. Eur. Ceram. Soc. (2024). https://doi.org/10.1016/j.jeurceramsoc.2023.10.02310.1016/j.jeurceramsoc.2023.10.023 ↗
13. Hu, J., Yang, Q., Zhu, S., Zhang, Y., Yan, D., Gan, K., Li, Z.: Superhard bulk high-entropy carbides with enhanced toughness via metastable in-situ particles. Nat. Commun. 14(1), 5717 (2023). https://doi.org/10.1038/s41467-023-41481-610.1038/s41467-023-41481-6 ↗
14. Xiang, H., Xing, Y., Dai, F., Wang, H., Su, L., Miao, L., Zhang, G., Wang, Y., Qi, X., Yao, L., Wang, H., Zhao, B., Li, J., Zhou, Y.: High-entropy ceramics: present status, challenges, and a look forward. J Adv Ceram 10(3), 385–441 (2021). https://doi.org/10.1007/s40145-021-0477-y10.1007/s40145-021-0477-y ↗
15. Hua, X., Mao, X., Yang, S., Zhang, M., Fu, L., Han, C., Huang, A., Gu, H.: Entropy regulation in spinel oxide with narrowed band gap and lattice distortion toward high-temperature infrared radiation. J. Alloy. Compd. 994, 174631 (2024). https://doi.org/10.1016/j.jallcom.2024.17463110.1016/j.jallcom.2024.174631 ↗
16. Li, Z., Wang, Z., Wu, Z., Xu, B., Zhao, S., Zhang, W., Lin, N.: Phase, microstructure and related mechanical properties of a series of (NbTaZr)C-based high entropy ceramics. Ceram. Int. 47(10), 14341–14347 (2021). https://doi.org/10.1016/j.ceramint.2021.02.01310.1016/j.ceramint.2021.02.013 ↗
17. Shi, Z., Zhang, X., Zheng, Z., Feng, X., Fang, Z., Tang, B.: Phase compositions, microstructures, and microwave dielectric properties of novel high-entropy spinel-structured MAl2O4 Ceramics. J. Alloy. Compd. 1004, 175714 (2024). https://doi.org/10.1016/j.jallcom.2024.17571410.1016/j.jallcom.2024.175714 ↗
18. Feng, L., Fahrenholtz, W.G., Hilmas, G.E., Monteverde, F.: Effect of Nb content on the phase composition, densification, microstructure, and mechanical properties of high-entropy boride ceramics. J. Eur. Ceram. Soc. 41(1), 92–100 (2021). https://doi.org/10.1016/j.jeurceramsoc.2020.08.05810.1016/j.jeurceramsoc.2020.08.058 ↗
19. Aamlid, S.S., Oudah, M., Rottler, J., Hallas, A.M.: Understanding the role of entropy in high entropy oxides. J. Am. Chem. Soc. 145(11), 5991–6006 (2023). https://doi.org/10.1021/jacs.2c1160810.1021/jacs.2c11608 ↗
20. Ramprasad, R., Batra, R., Pilania, G., Mannodi-Kanakkithodi, A., Kim, C.: Machine learning in materials informatics: recent applications and prospects. npj Comput. Mater. (2017). https://doi.org/10.1038/s41524-017-0056-510.1038/s41524-017-0056-5 ↗
21. Yue, D., Feng, Y., Liu, X., Yin, J., Zhang, W., Guo, H., Su, B., Lei, Q.: Prediction of energy storage performance in polymer composites using high-throughput stochastic breakdown simulation and machine learning. Adv. Sci. (2022). https://doi.org/10.1002/advs.20210577310.1002/advs.202105773 ↗
22. Li, W., Yang, T., Liu, C., Huang, Y., Chen, C., Pan, H., Xie, G., Tai, H., Jiang, Y., Wu, Y., Kang, Z., Chen, L., Su, Y., Hong, Z.: Optimizing piezoelectric nanocomposites by high-throughput phase-field simulation and machine learning. Adv. Sci. (2022). https://doi.org/10.1002/advs.20210555010.1002/advs.202105550 ↗
23. Jin, Y., He, L., Wen, Z., Mortazavi, B., Guo, H., Torrent, D., Djafari-Rouhani, B., Rabczuk, T., Zhuang, X., Li, Y.: Intelligent on-demand design of phononic metamaterials. Nanophotonics. (2022). https://doi.org/10.1515/nanoph-2021-063910.1515/nanoph-2021-0639 ↗
24. Liu, J., Wang, A., Gao, P., Bai, R., Liu, J., Du, B., Fang, C.: Machine learning-based crystal structure prediction for high-entropy oxide ceramics. J. Am. Ceram. Soc. (2024). https://doi.org/10.1111/jace.1951810.1111/jace.19518 ↗
25. Nouira, A., Sokolovska, N., Crivello, J.-C.: CrystalGAN: learning to discover crystallographic structures with generative adversarial networks. https://doi.org/10.48550/arXiv.1810.1120310.48550/arXiv.1810.11203 ↗
26. Lee, S.Y., Byeon, S., Kim, H.S., Jin, H., Lee, S.: Deep learning-based phase prediction of high-entropy alloys: optimization, generation, and explanation. Mater. Des. 197, 109260 (2021). https://doi.org/10.1016/j.matdes.2020.10926010.1016/j.matdes.2020.109260 ↗
27. Dan, Y., Zhao, Y., Li, X., Li, S., Hu, M., Hu, J.: Generative Adversarial Networks (GAN) based efficient sampling of chemical composition space for inverse design of inorganic materials. npj Comput. Mater. 6(1), 84 (2020). https://doi.org/10.1038/s41524-020-00352-010.1038/s41524-020-00352-0 ↗
28. Li, Z., Nash, W.T., O’Brien, S.P., Qiu, Y., Gupta, R.K., Birbilis, N.: cardiGAN: a generative adversarial network model for design and discovery of multi principal element alloys. J. Mater. Sci. Technol. 125, 81–96 (2022). https://doi.org/10.1016/j.jmst.2022.03.00810.1016/j.jmst.2022.03.008 ↗
29. Ye, Y., Li, Y., Ouyang, R., et al.: Improving machine learning based phase and hardness prediction of high-entropy alloys by using gaussian noise augmented data. Comput. Mater. Sci. 223, 112140 (2023)10.1016/j.commatsci.2023.112140 ↗
30. Chen, C., Zhou, H., Long, W., et al.: Phase prediction for high-entropy alloys using generative adversarial network and active learning based on small datasets. Sci. China Technol. Sci. 66(12), 3615–3627 (2023)10.1007/s11431-023-2399-2 ↗
31. Zhang, J., Xu, B., Xiong, Y., Ma, S., Wang, Z., Wu, Z., Zhao, S.: Design high-entropy carbide ceramics from machine learning. npj Comput. Mater. (2022). https://doi.org/10.1038/s41524-021-00678-310.1038/s41524-021-00678-3 ↗
32. Chandrashekar, G., Sahin, F.: A survey on feature selection methods. Comput. Electr. Eng. (2014). https://doi.org/10.1016/j.compeleceng.2013.11.02410.1016/j.compeleceng.2013.11.024 ↗