Accelerated design of Fe-based soft magnetic materials using machine learning and stochastic optimization

• Published in: Acta materialia Vol. 194; pp. 144 - 155
• Main Authors: Wang, Yuhao, Tian, Yefan, Kirk, Tanner, Laris, Omar, Ross, Joseph H., Noebe, Ronald D., Keylin, Vladimir, Arróyave, Raymundo
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.08.2020
• Subjects: machine learning; materials design; nanocrystalline; soft magnetic properties; nanocrystalline; materials design; soft magnetic properties; machine learning
• ISSN: 1359-6454; 1873-2453
• DOI: 10.1016/j.actamat.2020.05.006

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.