About this Abstract |
Meeting |
MS&T22: Materials Science & Technology
|
Symposium
|
Tackling Structural Materials Challenges for Advanced Nuclear Reactors
|
Presentation Title |
Convolutional Neural Networks Screening Radiation-resistant High Entropy Alloys |
Author(s) |
Penghui Cao |
On-Site Speaker (Planned) |
Penghui Cao |
Abstract Scope |
The emergent multi-principal element alloys (MPEAs), commonly known as high entropy alloys, provide a vast compositional space to search for radiation-resistant materials for advanced nuclear reactor application. However, how to efficiently identify optimal compositions is a grand challenge. This talk will present a convolutional neural network model that can accurately and efficiently predict path-dependent defect migration energy barriers—the critical parameters to radiation defect evolution and growth in MPEAs. The success of the machine learning model makes it promise to develop a database of defect diffusion barriers for different multicomponent alloy systems, which would accelerate alloy screening for the discovery of new compositions with desirable radiation performance. |