Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Predicting nucleation near the spinodal in the Ising model using machine
  learning   We use a Convolutional Neural Network (CNN) and two logistic regression
models to predict the probability of nucleation in the two-dimensional Ising
model. The three models successfully predict the probability for the Nearest
Neighbor Ising model for which classical nucleation is observed. The CNN
outperforms the logistic regression models near the spinodal of the Long Range
Ising model, but the accuracy of its predictions decreases as the quenches
approach the spinodal. Occlusion analysis suggests that this decrease is due to
the vanishing difference between the density of the nucleating droplet and the
background. Our results are consistent with the general conclusion that
predictability decreases near a critical point.

34
The categories of this paper are: chem-ph
26
The categories of this paper are: bayes-an
31
The categories of this paper are: ao-sci
2
The categories of this paper are: math
28
The categories of this paper are: alg-geom
23
The categories of this paper are: solv-int
18
The categories of this paper are: econ
16
The categories of this paper are: stat
13
The categories of this paper are: quant-ph
21
The categories of this paper are: adap-org
29
The categories of this paper are: funct-an
10
The categories of this paper are: q-bio
8
The categories of this paper are: hep-ex
6
The categories of this paper are: astro-ph
33
The categories of this paper are: plasm-ph
27
The categories of this paper are: comp-gas