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We found that Bidirectional LSTM and Transformer can classify differentphases of condensed matter models and determine the phase transition points bylearning features in the Monte Carlo raw data before equilibrium. Our methodcan significantly reduce the time and computational resources required forprobing phase transitions as compared to the conventional Monte Carlosimulation. We also provide evidence that the method is robust and theperformance of the deep learning model is insensitive to the type of input data(we tested spin configurations of classical models and green functions of aquantum model), and it also performs well in detecting Kosterlitz-Thoulessphase transitions.

Rapid detection of phase transitions from Monte Carlo samples before equilibrium