Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Leaving the Valley: Charting the Energy Landscape of Metal/Organic
  Interfaces via Machine Learning   The rich polymorphism exhibited by inorganic/organic interfaces is a major
challenge for materials design. In this work we present a method to efficiently
explore the potential energy surface and predict the formation energies of
polymorphs and defects. This is achieved by training a machine learning model
on a list of only 100 candidate structures that are evaluated via
dispersion-corrected Density Functional Theory (DFT) calculations. We
demonstrate the power of this approach for tetracyanoethylene on Ag(100) and
explain the anisotropic ordering that is observed experimentally.

15
The categories of this paper are: nucl-ex
19
The categories of this paper are: eess
20
The categories of this paper are: acc-phys
12
The categories of this paper are: nucl-th
16
The categories of this paper are: stat
25
The categories of this paper are: patt-sol
22
The categories of this paper are: chao-dyn
32
The categories of this paper are: atom-ph
6
The categories of this paper are: astro-ph
10
The categories of this paper are: q-bio
17
The categories of this paper are: q-fin
37
The categories of this paper are: supr-con
14
The categories of this paper are: math-ph
8
The categories of this paper are: hep-ex
31
The categories of this paper are: ao-sci
26
The categories of this paper are: bayes-an