Determination of the Dzyaloshinskii-Moriya interaction using pattern recognition and machine learning

TL;DR

This paper demonstrates that machine learning, specifically convolutional neural networks, can accurately extract the Dzyaloshinskii-Moriya interaction and magnetic anisotropy from magnetic domain images, streamlining materials research.

Contribution

It introduces a novel machine learning approach to determine magnetic parameters from images, reducing experimental complexity in materials science.

Findings

Estimated DM exchange constants agree with experimental values.

The system can independently determine magnetic anisotropy distribution.

Pattern recognition simplifies experimental procedures in magnetic materials research.

Abstract

Machine learning is applied to a large number of modern devices that are essential in building energy efficient smart society. Audio and face recognition are among the most well-known technologies that make use of such artificial intelligence. In materials research, machine learning is adapted to predict materials with certain functionalities, an approach often referred to as materials informatics. Here we show that machine learning can be used to extract material parameters from a single image obtained in experiments. The Dzyaloshinskii-Moriya (DM) interaction and the magnetic anisotropy distribution of thin film heterostructures, parameters that are critical in developing next generation storage class magnetic memory technologies, are estimated from a magnetic domain image. Micromagnetic simulation is used to generate thousands of random images for training and model validation. A convolutional neural network system is employed as the learning tool. The DM exchange constant of typical Co-based thin film heterostructures is studied using the trained system: the estimated values are in good agreement with experiments. Moreover, we show that the system can independently determine the magnetic anisotropy distribution, demonstrating the potential of pattern recognition. This approach can considerably simplify experimental processes and broaden the scope of materials research.