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Driven by the imperative demand for extreme−environment materials in strategic sectors—including aerospace, deep−sea exploration, and advanced nuclear energy—traditional material systems are approaching their physical limits under synergistic conditions of ultra−high temperatures, severe corrosion, and high stress. High−entropy ceramics (HECs), characterized by a extremely vast compositional space and exceptional stability in extreme environments, are regarded as a pivotal class of next−generation strategic materials. However, the traditional "trial−and−error" R&D paradigm is trapped by the vast compositional landscape in a "combinatorial explosion", rendering development cycles prolonged and inefficient. Consequently, the targeted and efficient design of HECs remains a critical bottleneck hindering engineering application. A novel paradigm is systematically analyzed for the intelligent design of ultra−high temperature ceramics (AI4UHTC). With ultra−high temperature HECs selected as representative materials, the deep integration of high−throughput computation and machine learning (ML) within this paradigm is examined. A fundamental shift in the R&D workflow—from "empirical trial−and−error" to a "knowledge−assisted, data−driven" framework—is thereby driven. The evolutionary logic of HEC design paradigms—progressing from empirical and knowledge−based approaches to data−driven and intelligent strategies—is systematically elucidated. Specifically, the robust capabilities of ML are highlighted regarding accurate synthesizability prediction, the design and optimization of critical properties (mechanical, thermal, and chemical), and multi−objective synergy, supported by concrete application cases. Concurrently, challenges currently confronting intelligent design are critically analyzed regarding data ecosystems, model interpretability, process integration, and the closure of the computation−experiment loop. Ultimately, the establishment of the "Data−Model−Knowledge−Wisdom" autonomous evolutionary pathway and the "AI Multi−Agent" system will efficiently advance software autonomy for the intelligent design of ultra−high−temperature high−entropy ceramics and realize typical application demonstrations of AI4UHTC.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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