|About this Abstract
||MS&T22: Materials Science & Technology
||High Entropy Materials: Concentrated Solid Solutions, Intermetallics, Ceramics, Functional Materials and Beyond III
||Accelerated Discovery of Refractory High Entropy Materials by Machine Learning and High Throughput Experiments
||Kun Wang, Yonggang Yan
|On-Site Speaker (Planned)
Refractory high entropy materials (RHEMs), including refractory alloys and ultrahigh temperature ceramics, have exhibited excellent properties as potential high-temperature structural materials in extreme environments such as nuclear reactors and hypersonic environments etc. However, it would be extremely challenging to develop such materials using the conventional trial and error experiment due to the massive compositional space. Herein, we employed machine learning (ML) to rapidly and accurately screen the compositions of RHEMs with desirable properties. In particular, 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.