Accelerated design of Fe-based soft magnetic materials using machine learning and stochastic optimization. (1st August 2020) - Wang, Yuhao; Tian, Yefan; Kirk, Tanner; Laris, Omar; Ross, Joseph H.; Noebe, Ronald D.; Keylin, Vladimir; Arróyave, Raymundo
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.