  As machine learning becomes increasingly important in engineering and
science, it is inevitable that machine learning techniques will be applied to
the investigation of materials, and in particular the structural phase
transitions common in ferroelectric materials. Here, we build and train an
artificial neural network to accurately predict the energy change associated
with atom displacements and use the trained artificial neural network in
Monte-Carlo simulations on ferroelectric materials to investigate their phase
transitions. We apply this approach to two-dimensional monolayer SnTe and show
that it can indeed be used to simulate the phase transitions and predict the
transition temperature. The artificial neural network, when viewed as a
universal mathematical structure, can be readily transferred to the
investigation of other ferroelectric materials when training data generated
with ab initio methods are available.
