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Machine learning techniques exhibit significant performance in discriminatingdifferent phases of matter and provide a new avenue for studying phasetransitions. We investigate the phase transitions of three dimensional$q$-state Potts model on cubic lattice by using a transfer learning approach,Domain Adversarial Neural Network (DANN). With the unique neural networkarchitecture, it could evaluate the high-temperature (disordered) andlow-temperature (ordered) phases, and identify the first and second order phasetransitions. Meanwhile, by training the DANN with a few labeled configurations,the critical points for $q=2,3,4$ and $5$ can be predicted with high accuracy,which are consistent with those of the Monte Carlo simulations. These findingswould promote us to learn and explore the properties of phase transitions inhigh-dimensional systems.

Applications of Domain Adversarial Neural Network in phase transition of 3D Potts model