Recent advances in lattice thermal conductivity calculation using machine-learning interatomic potentials

Abstract

The accuracy of the interatomic potential functions employed in molecular dynamics (MD) simulation is one of the most important challenges of this technique. In contrast, the high accuracy ab initio quantum simulation cannot be an alternative to MD due to its high computational cost. In the meantime, the machine learning approach has been able to compromise these two numerical techniques. This work unveils how the MD interatomic potentials have been improved through training over ab initio datasets and are able to well calculate phononic thermal transport of materials. Therefore, this powerful tool allows the quantum computational order accuracy with a timescale in the order of classical computations. Besides, the thermal conductivity of a few 2D and 3D structures, which have been calculated using machine learning interatomic potentials (MLIPs), is presented and compared with experimental and quantum counterparts. Finally, it is discussed that how MLIPs can be developed not only to estimate other properties of pristine materials, such as mechanical properties, but also to predict the properties of defective materials.