| About this Abstract |
| Meeting |
2022 TMS Annual Meeting & Exhibition
|
| Symposium
|
Advanced Magnetic Materials for Sensors, Power, and Multifunctional Applications
|
| Presentation Title |
Accelerated Design of Fe-based Soft Magnetic Materials Using Machine Learning and Stochastic Optimization |
| Author(s) |
Raymundo Arroyave, Yuhao Wang, Tanner Kirk, Yefan Tian, Joseph H. Ross, Ron Noebe |
| On-Site Speaker (Planned) |
Raymundo Arroyave |
| Abstract Scope |
Machine learning was utilized to efficiently boost the development of soft magnetic materials. The design process includes building a database composed of published experimental results, applying machine learning methods on the database, identifying the trends of magnetic properties in soft magnetic materials, and accelerating the design of next-generation soft magnetic nanocrystalline materials through the use of numerical optimization. Machine learning regression models were trained to predict magnetic saturation (BS), coercivity (HC) and magnetostriction (λ), with a stochastic optimization framework being used to further optimize the corresponding magnetic properties. To verify the feasibility of the machine learning model, several optimized soft magnetic materials – specified in terms of compositions and thermomechanical treatments – have been predicted and then prepared and tested, showing good agreement between predictions and experiments, proving the reliability of the designed model. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Magnetic Materials, Machine Learning, Computational Materials Science & Engineering |