About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
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Symposium
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AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
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Presentation Title |
Accelerated Discovery of Ultra-high Temperature High Entropy Ceramics by Machine Learning and High Throughput Experiments |
Author(s) |
Kun Wang, Yonggang Yan |
On-Site Speaker (Planned) |
Kun Wang |
Abstract Scope |
Machine learning (ML) methods have been successfully applied to predict phase formation and properties of novel materials, such as high entropy materials. However, the application of ML approach in ultra-high temperature high entropy ceramics which are potential high-temperature structural materials in extreme environments such as nuclear reactors and hypersonic environments etc., remains limited researched due to the serious data scarcity as well as data quality issues. Herein, the high-throughput experiments were employed to generate high-quality dataset for ML training, because the experiment was conducted under the same conditions and by the same researcher. The experimental validation is performed to examine the performance of the ML model. In addition, the ML is also applied to discover the most relevant input features with respect to the output properties, giving rise to an inverse understanding of the underlying physical mechanisms. In particular, we discovered an empirical phase classification rule for high entropy diborides. |
Proceedings Inclusion? |
Planned: |
Keywords |
Ceramics, Machine Learning, Other |