  Molecular dynamics simulations have been extensively used to predict thermal
properties, but simulating different phases with similar precision using a
unified force field is often difficult, due to the lack of accurate and
transferrable interatomistic potential fields. As a result, this issue has
become a major barrier to predicting the phase change of materials and their
transport properties with atomistic-level modeling techniques. Recently,
machine learning based algorithms have emerged as promising tools to develop
accurate potentials for molecular dynamics simulations. In this work, we
approach the problem of predicting the thermal conductivity of silicon in
different phases by performing molecular dynamics simulations with a deep
neural network potential. This neural network potential is trained with
ab-initio data of silicon in the crystalline, liquid and amorphous phases. The
accuracy of our potential is first validated through reproducing the atomistic
structures during the phase transition, where other empirical potentials
usually fail. The thermal conductivity of different phases is then calculated,
showing a good agreement with the experimental results and ab-initio
calculation results. Our work shows that a unified neural network-based
potential can be a promising tool for studying phase change and thermal
transport of materials with high accuracy.
