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State-of-the-art machine learning techniques promise to become a powerful tool in statistical mechanics via their capacity to distinguish different phases of matter in an automated way.
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An alternative choice to identify the location of the phase transition for a finite system size is the parametric location of the inflection point of the prediction function F F . At the inflection point the derivative is maximal, i.e. moving the coupling parameters slightly towards any one of the two phases leads to the largest possible change in the prediction. Such an indicator of the phase transition is commonly used e.g. when considering susceptibility measurements of conventional order parameters. For our setup we find almost no distinguishable difference in the location and finite-size scaling of the inflection point and the point at which the prediction function F = 1 / 2 F=1/2
Cited in the paper.
S. S. Schoenholz, E. D. Cubuk, D. M. Sussman, E. Kaxiras, and A. J. Liu, A structural approach to relaxation in glassy liquids, Nat. Phys. 12
2016
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Y. Otsuka, S. Yunoki, and S. Sorella, Universal quantum criticality in the metal-insulator transition of two-dimensional interacting dirac electrons, Phys. Rev. X 6
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P. Broecker and S. Trebst, Entanglement and the fermion sign problem in auxiliary field quantum Monte Carlo simulations, Phys. Rev. B 94
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