Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Machine Learning Phases of Strongly Correlated Fermions   Machine learning offers an unprecedented perspective for the problem of
classifying phases in condensed matter physics. We employ neural-network
machine learning techniques to distinguish finite-temperature phases of the
strongly correlated fermions on cubic lattices. We show that a three
dimensional convolutional network trained on auxiliary field configurations
produced by quantum Monte Carlo simulations of the Hubbard model can correctly
predict the magnetic phase diagram of the model at the average density of one
(half filling). We then use the network, trained at half filling, to explore
the trend in the transition temperature as the system is doped away from half
filling. This transfer learning approach predicts that the instability to the
magnetic phase extends to at least 5% doping in this region. Our results pave
the way for other machine learning applications in correlated quantum many-body
systems.

22
The categories of this paper are: chao-dyn
24
The categories of this paper are: dg-ga
25
The categories of this paper are: patt-sol
19
The categories of this paper are: eess
3
The categories of this paper are: physics
32
The categories of this paper are: atom-ph
29
The categories of this paper are: funct-an
1
The categories of this paper are: cs
16
The categories of this paper are: stat
30
The categories of this paper are: q-alg
11
The categories of this paper are: hep-lat
7
The categories of this paper are: hep-th
23
The categories of this paper are: solv-int
9
The categories of this paper are: nlin
2
The categories of this paper are: math
12
The categories of this paper are: nucl-th