  First-principles based modeling on phonon dynamics and transport using
density functional theory and Boltzmann transport equation has proven powerful
in predicting thermal conductivity of crystalline materials, but it remains
unfeasible for modeling complex crystals and disordered solids due to the
prohibitive computational cost to capture the disordered structure, especially
when the quasiparticle "phonon" model breaks down. Recently, machine-learning
regression algorithms show great promises for building high-accuracy potential
fields for atomistic modeling with length and time scales far beyond those
achievable by first-principles calculations. In this work, using both
crystalline and amorphous silicon as examples, we develop machine learning
based potential fields for predicting thermal conductivity. The machine
learning based interatomic potential is derived from density functional theory
calculations by stochastically sampling the potential energy surface in the
configurational space. The thermal conductivities of both amorphous and
crystalline silicon are then calculated using equilibrium molecular dynamics,
which agree well with experimental measurements. This work documents the
procedure for training the machine-learning based potentials for modeling
thermal conductivity, and demonstrates that machine-learning based potential
can be a promising tool for modeling thermal conductivity of both crystalline
and amorphous materials with strong disorder.

Materials Science;Computational Physics