Image-based prediction and optimization of hysteresis properties of nanocrystalline permanent magnets using deep learning

We demonstrate the use of model order reduction and neural networks for estimating the hysteresis properties of nanocrystalline permanent magnets from microstructure. With a data-driven approach, we learn the demagnetization curve from data-sets created by grain growth and micromagnetic simulations. We show that the granular structure of a magnet can be encoded within a low-dimensional latent space. Latent codes are constructed using a variational autoencoder. The mapping of structure code to hysteresis properties is a multi-target regression problem. We apply deep neural network and use parameter sharing, in order to predict anchor points along the demagnetization curves from the magnet's structure code. We present a proof of concept for microstructure design of nanocrystalline Nd₂Fe₁₄B permanent magnets using image-based machine learning. We show how new grain structures can be generated by interpolation between two points in the latent space and demonstrate inverse design. We show how to compute the granular microstructure that will give a demagnetization curve with a given remanence, squareness, and coercivity through optimization in latent space.