Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Interpretable and unsupervised phase classification   Fully automated classification methods that yield direct physical insights
into phase diagrams are of current interest. Here, we demonstrate an
unsupervised machine learning method for phase classification which is rendered
interpretable via an analytical derivation of its optimal predictions and
allows for an automated construction scheme for order parameters. Based on
these findings, we propose and apply an alternative, physically-motivated,
data-driven scheme which relies on the difference between mean input features.
This mean-based method is computationally cheap and directly interpretable. As
an example, we consider the physically rich ground-state phase diagram of the
spinless Falicov-Kimball model.

26
The categories of this paper are: bayes-an
37
The categories of this paper are: supr-con
35
The categories of this paper are: mtrl-th
16
The categories of this paper are: stat
28
The categories of this paper are: alg-geom
21
The categories of this paper are: adap-org
30
The categories of this paper are: q-alg
9
The categories of this paper are: nlin
36
The categories of this paper are: cmp-lg
29
The categories of this paper are: funct-an
8
The categories of this paper are: hep-ex
22
The categories of this paper are: chao-dyn
10
The categories of this paper are: q-bio
19
The categories of this paper are: eess
27
The categories of this paper are: comp-gas
32
The categories of this paper are: atom-ph