Vibrational Properties of Metastable Polymorph Structures by Machine Learning

Despite
vibrational properties being critical for the ab initio prediction of finite-temperature stability as well as thermal conductivity
and other 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 121 different
mechanically stable structures of KZnF3 reaches a mean
absolute error of 0.17 eV/Å2 for the interatomic force
constants, and it is less expensive than training the complete force
field for such compounds. 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.