Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Vibrational properties of metastable polymorph structures by machine
  learning   Despite vibrational properties being critical for the ab initio prediction of
the finite temperature stability and transport properties of solids, their
inclusion in ab initio materials repositories has been hindered by expensive
computational requirements. Here we tackle the challenge, by showing that a
good estimation of force constants and vibrational properties can be quickly
achieved from the knowledge of atomic equilibrium positions using machine
learning. A random-forest algorithm trained on only 121 metastable structures
of KZnF$_3$ reaches a maximum absolute error of 0.17 eV/$\textrm\AA^2$ for the
interatomic force constants, and it is much less expensive than training the
complete force field for such compound. The predicted force constants are then
used to estimate phonon spectral features, heat capacities, vibrational
entropies, and vibrational free energies, which compare well with the ab initio
ones. The approach can be used for the rapid estimation of stability at finite
temperatures.

13
The categories of this paper are: quant-ph
15
The categories of this paper are: nucl-ex
32
The categories of this paper are: atom-ph
31
The categories of this paper are: ao-sci
34
The categories of this paper are: chem-ph
26
The categories of this paper are: bayes-an
3
The categories of this paper are: physics
18
The categories of this paper are: econ
8
The categories of this paper are: hep-ex
11
The categories of this paper are: hep-lat
28
The categories of this paper are: alg-geom
5
The categories of this paper are: gr-qc
29
The categories of this paper are: funct-an
35
The categories of this paper are: mtrl-th
25
The categories of this paper are: patt-sol
7
The categories of this paper are: hep-th