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Meeting MS&T23: Materials Science & Technology
Symposium Leveraging Integrated Computational Materials Engineering for High-fidelity Physics-based and Machine Learning Models
Presentation Title New Refractory High Entropy Alloys Discovery by Physics Discovery
Author(s) Lele Luan, Chen Shen, Scott Oppenheimer, Feng Zhang, Ryan Jacobs, Liping Wang
On-Site Speaker (Planned) Lele Luan
Abstract Scope Refractory high-entropy alloys (RHEAs) have attracted extensive interest in aerospace application after its first development a decade ago and various machine learning (ML) algorithms have been applied to accelerate new material discovery. In this work, we proposed the application of a new ML concept, physics discovery, to the modeling and design of RHEAs. Physics discovery distilled a human-readable equation from data. Prior knowledge of material characteristics and mechanism were utilized as modeling constraints to ensure physical interpretability. The physics discovery model was built by taking a new RHEA dataset, 86 samples with different element compositions, published very recently. The discovered human-readable equations not only help to understand the mechanism of high-hardness of RHEAs, but also to predict the new materials (element compositions) with higher hardness. This works can be treated a preliminary attempt to build interpretable ML model for better understanding of RHEAs.


Application of Machine Learning Framework in Predicting Creep Response of High Temperature Alloys
Hybrid Simulation Method Based on Molecular Dynamics and Machine Learning to Improve Property Prediction with Lower Computational Cost in Complex System
New Refractory High Entropy Alloys Discovery by Physics Discovery
Novel Convolutional-Recurrent Hybrid Neural Network for Predicting Fission Gas Release in UO2 Nuclear Fuel
Robotic Bending of Craniomaxillofacial Graft Fixation Plates
Simulating Macroscale Microstructures Using Advanced Programming and Numerical Methods

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