Empirical interatomic potentials optimized for phonon properties

Abstract

Molecular dynamics simulations have been extensively used to study phonons and gain insight, but direct comparisons to experimental data are often difficult, due to a lack of accurate empirical interatomic potentials for different systems. As a result, this issue has become a major barrier to realizing the promise associated with advanced atomistic-level modeling techniques. Here, we present a general method for specifically optimizing empirical interatomic potentials from ab initio inputs for the study of phonon transport properties, thereby resulting in phonon optimized potentials. The method uses a genetic algorithm to directly fit the empirical parameters of the potential to the key properties that determine whether or not the atomic level dynamics and most notably the phonon transport are described properly. A framework has been developed that can optimize the potentials needed to more accurately study phonons using molecular dynamics. Molecular dynamics simulations are an indispensable tool for studying how atoms interact. Despite their widespread use, however, it is often difficult to determine the potentials needed to accurately describe the various interactions involved for phonons, which are the excitations that underpin physical properties such as thermal conductivity. An international team of researchers led by professor Asegun Henry from the Georgia Institute of Technology presents an approach, based on a genetic algorithm, that can optimise the empirical interatomic potentials for phonons from first principles inputs, that can be used in classical molecular dynamics simulations. And although they demonstrate this method with semiconducting silicon and germanium, it should be extendable to alloys and disordered systems.

Terumasa Tadano

Researcher of Materials Science

My research interests include development of computational methods and softwares for predicting thermal properties of solids, and application of machine-learning methods to material science study