4.6 Article

Global exploration of phase behavior in frustrated Ising models using unsupervised learning techniques

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Summary: Unsupervised learning techniques, specifically convolutional autoencoders, are used to classify different phases of the J(1)-J(2) antiferromagnetic Ising model on the honeycomb lattice without the need for labels or prior knowledge. Different training methods for the autoencoders are proposed and evaluated to discriminate between distinct magnetic phases, including cases of high-temperature or random training data. The capability of the autoencoder to detect ground state degeneracy through reconstruction error is also analyzed.

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