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

• Development of a systematic data-driven methodology for HECs design. • Experimental validation of computational predictions through bulk sample synthesis. • Practical use of these materials as EBCs with exceptional high-temperature stability. 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 with targeted performance attributes, thus offering new avenues for advancing high-performance ceramic coatings across various applications. Through this screening process, we successfully identified and synthesized five rare earth silicate materials. Experimental measurements indicate that these materials have the thermal conductivity minimum range from 0.93 to 1.22 W·m −1 ·K −1 , with an average coefficient of thermal expansion between room temperature and 1500 °C measured at (3.14 ∼ 3.84 × 10 −6 K −1 ). 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 (referred to as YbYErLuDy)) was selected for evaluating coating application performance, with samples prepared using Atmospheric Plasma Spraying (APS) technology, while investigating the high-temperature stability of Si/HEC composite coatings and assessing the differences in thermal conductivity between the (YbYErLuDy) coating and substrate materials.

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Publication Details

Journal
Materials & Design
Published
2025-06-19
DOI
https://doi.org/10.1016/j.matdes.2025.114277
Citations
3
Primary Topic
High Entropy Alloys Studies
Type
article
Field-Weighted Citation Impact
2.14
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article

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

Lei Zheng, Hongbo Guo, Yidan Wang, Kang Peng et al.
3 citations
Materials & Design
High Entropy Alloys Studies
2.14
article

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

Lei Zheng, Hongbo Guo, Yidan Wang, Kang Peng, Jian He, Dongrui Liu, Zhen Li
article en
3 citations

Abstract

• Development of a systematic data-driven methodology for HECs design. • Experimental validation of computational predictions through bulk sample synthesis. • Practical use of these materials as EBCs with exceptional high-temperature stability. 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 with targeted performance attributes, thus offering new avenues for advancing high-performance ceramic coatings across various applications. Through this screening process, we successfully identified and synthesized five rare earth silicate materials. Experimental measurements indicate that these materials have the thermal conductivity minimum range from 0.93 to 1.22 W·m −1 ·K −1 , with an average coefficient of thermal expansion between room temperature and 1500 °C measured at (3.14 ∼ 3.84 × 10 −6 K −1 ). 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 (referred to as YbYErLuDy)) was selected for evaluating coating application performance, with samples prepared using Atmospheric Plasma Spraying (APS) technology, while investigating the high-temperature stability of Si/HEC composite coatings and assessing the differences in thermal conductivity between the (YbYErLuDy) coating and substrate materials.

Materials & DesignVol. 256
Beijing Academy of Artificial Intelligence (CN), Beihang University (CN)
Openalex Percentile: Top 13%
High Entropy Alloys Studies
2.14
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