Accelerated design of Fe-based soft magnetic materials using machine learning and stochastic optimization. (1st August 2020)

• Record Type: Journal Article
• Authors: Wang, Yuhao; Tian, Yefan; Kirk, Tanner; Laris, Omar; Ross, Joseph H.; Noebe, Ronald D.; Keylin, Vladimir; Arróyave, Raymundo
• Is Part Of: Acta materialia. Volume 194(2020)
• Page Start: 144
• Page End: 155
• Publication Date: 2020-08-01
• Subjects: machine learning -- soft magnetic properties -- nanocrystalline -- materials design; Materials -- Periodicals; Materials science -- Periodicals; Materials -- Mechanical properties -- Periodicals; Metallurgy -- Periodicals; Chemistry, Inorganic -- Periodicals; 620.112
• DOI: 10.1016/j.actamat.2020.05.006
• Languages: English
• ISSNs: 1359-6454

Abstract

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.