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arXiv · 2002.05225

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

Yuhao Wang·Yefan Tian·Tanner Kirk·Omar Laris·Joseph H. Ross, Jr.·Ronald D. Noebe·Vladimir Keylin·Raymundo Arróyave
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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 ($B_S$), coercivity ($H_C$) 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.

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Yuhao Wang, Yefan Tian, Tanner Kirk, Omar Laris, Joseph H. Ross, Jr., Ronald D. Noebe, Vladimir Keylin, Raymundo Arróyave. 2020-05-04. Accelerated design of Fe-based soft magnetic materials using machine learning and stochastic optimization. https://doi.org/10.1016/j.actamat.2020.05.006

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