Principal component analysis for fermionic critical points

doi:10.1103/PhysRevB.96.195138

Principal component analysis for fermionic critical points

2017-11-20

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DOI：10.1103/PhysRevB.96.195138

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We use determinant quantum Monte Carlo (DQMC), in combination with the principal component analysis (PCA) approach to unsupervised learning, to extract information about phase transitions in several of the most fundamental Hamiltonians describing strongly correlated materials. We first explore the zero-temperature antiferromagnet to singlet transition in the periodic Anderson model, theMott insulating transition in the Hubbard model on a honeycomb lattice, and the magnetic transition in the 1/6-filled Lieb lattice. We then discuss the prospects for learning finite temperature superconducting transitions in the attractive Hubbard model, for which there is no sign problem. Finally, we investigate finite temperature charge density wave ( CDW) transitions in the Holstein model, where the electrons are coupled to phonon degrees of freedom, and carry out a finite size scaling analysis to determine T-c. We examine the different behaviors associated with Hubbard-Stratonovich auxiliary field configurations on both the entire space-time lattice and on a single imaginary time slice, or other quantities, such as equal-time Green's and pair-pair correlation functions.

Keywords:

MONTE-CARLO SIMULATIONS

KONDO-LATTICE MODEL

SPIN-GLASS MODEL

HUBBARD-MODEL

NEURAL-NETWORKS

PHASE-DIAGRAM

GROUND-STATES

SIGN PROBLEM

SYSTEMS

FERROMAGNETISM

Principal component analysis for fermionic critical points

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