  Magnetism prediction is of great significance for Fe-based metallic glasses
(FeMGs), which have shown great commercial value. Theories or models
established based on condensed matter physics exhibit several exceptions and
limited accuracy. In this work, machine learning (ML) models learned from a
large amount of experimental data were trained based on eXtreme gradient
boosting (XGBoost), artificial neural networks (ANN), and random forest to
predict the magnetic properties of FeMGs. The XGBoost and ANN models exhibited
comparably excellent predictive performance, with R^2 >= 0.903, mean absolute
percentage error (MAPE) <= 6.17, and root mean squared error (RMSE) <= 0.098.
The trained ML models aggregate the influence of 13 factors, which is difficult
to achieve in traditional physical models. The influence of local structure,
which was represented by the experimental parameter of the supercooled liquid
region, presented a significant impact on the predictive performance of ML
models. The developed ML-based method here can predict the magnetic properties
of FeMGs by considering multiple factors simultaneously, including complex
local structures.
