Data-driven design of high-entropy silicate ceramics with low thermal conductivity

作者：Yidan Wang, Dongrui Liu, Zhen Li, Jian He, Lei Zheng, Kang Peng, Hongbo Guo · 发表于：Materials & Design · 年份：2025 · DOI：10.1016/j.matdes.2025.114277 · 被引用次数：8 · 研究领域：High Entropy Alloys Studies、Advanced materials and composites、Advanced ceramic materials synthesis

High-entropy rare earth silicate ceramics represent promising candidates for environmental barrier coatings (EBCs) due to their low thermal conductivity, compatible coefficients of thermal expansion (CTE), and high-temperature stability. In this study, we present a data-driven approach that integrates machine learning and experimental validation to efficiently screen and design high-entropy rare earth pyrosilicate ceramics with low thermal conductivity. Principal Component Analysis (PCA) and K-means clustering were applied to the sample dataset to predict the rare earth element compositions associated with low thermal conductivity in high-entropy rare earth silicate ceramics. Five HECs were successfully synthesized through screening, exhibiting minimum thermal conductivities ranging from 0.93 to 1.22 W·m −1 ·K −1 , and average coefficients of thermal expansion between 3.14 ∼ 3.84 × 10 −6 K −1 over the temperature range from room temperature to 1500 ℃. This validates the reliability of our machine learning predictions. The optimized material ((Yb 0.2 Y 0.2 Er 0.2 Lu 0.2 Dy 0.2 ) 2 Si 2 O 7 (Abbr. YbYErLuDy)) was selected for evaluating coating application performance. Si/HEC coatings were fabricated using atmospheric plasma spraying (APS), and high-temperature stability and thermal conductivity were systematically evaluated. The successful implementation of this data-driven approach demonstrates its potential in accelerating the design and development of novel EBCs materials w...