Accelerated design of Fe-based soft magnetic materials using machine learning and stochastic optimization
Abstract
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. Two rounds of optimization-testing iterations were conducted to search for better properties.
- Publication:
-
Acta Materialia
- Pub Date:
- August 2020
- DOI:
- 10.1016/j.actamat.2020.05.006
- arXiv:
- arXiv:2002.05225
- Bibcode:
- 2020AcMat.194..144W
- Keywords:
-
- machine learning;
- soft magnetic properties;
- nanocrystalline;
- materials design;
- Condensed Matter - Materials Science;
- Physics - Data Analysis;
- Statistics and Probability
- E-Print:
- Acta Mater. 194, 144 (2020)