  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.
