Making sense of coercivity in magnetic materials with machinelearning TUS_PR CommsPhys
to estimate coercivity as 0.09 T, which is reasonably close to the measured value of approximated 0.07 T. This result supports the robustness of the developed method. Another advantage of using the energy landscape in the reduced space is its sensitivity compared to the usual methods that can be highly affected by the steps of the external field as observed in the magnetisation curve . In our experiment, the coarser external field steps hinder the reversal process mechanism.
In simulations, coercivity can be calculated with high accuracy owing to the access of all variables in real materials; otherwise, there are many unknown parameters correlated with the microstructures. Our approach for describing the energy landscape using machine learning showed good results for both experimental and simulated data. Both shared similar shapes for the energy landscapes, as well as the explanation of variables and correlations between them.
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