  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 ($\lambda$), 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.
