  The application of twist engineering in van der Waals magnets has opened new
frontiers in the field of two-dimensional magnetism, yielding distinctive
magnetic domain structures. Despite the introduction of numerous theoretical
methods, limitations persist in terms of accuracy or efficiency due to the
complex nature of the magnetic Hamiltonians pertinent to these systems. In this
study, we introduce a deep-learning approach to tackle these challenges.
Utilizing customized, fully connected networks, we develop two
deep-neural-network kernels that facilitate efficient and reliable analysis of
twisted van der Waals magnets. Our regression model is adept at estimating the
magnetic Hamiltonian parameters of twisted bilayer CrI3 from its magnetic
domain images generated through atomistic spin simulations. The generative
model excels in producing precise magnetic domain images from the provided
magnetic parameters. The trained networks for these models undergo thorough
validation, including statistical error analysis and assessment of robustness
against noisy injections. These advancements not only extend the applicability
of deep-learning methods to twisted van der Waals magnets but also streamline
future investigations into these captivating yet poorly understood systems.
