An activation-relaxation technique study of two-level system impact on internal dissipation using DFT-based moment tensor potential

TL;DR

This study employs a machine-learned potential and activation-relaxation techniques to analyze two-level systems in amorphous silicon, revealing structural differences and the nature of TLSs.

Contribution

It introduces a DFT-trained machine-learned potential combined with ARTn to identify and classify TLSs, providing detailed atomistic insights.

Findings

TLS density involving bond-hopping is similar across potentials

Complex TLSs like Wooten-Winer-Weaire are twice as common with MTP

TLSs are mostly isolated and oscillate independently in MTP models

Abstract

We use a recently-developed machine-learned Moment Tensor Potential (MTP) trained on data generated with the density functional theory (DFT) and tailored to amorphous silicon coupled with the Activation-Relaxation Technique nouveau (ARTn) to identify and classify two-level systems (TLS). The samples generated using MTP recover experimental results and provide average structural and dissipative properties similar to those obtained with a modified Stillinger-Weber potential, including radial distribution function, defect concentration and internal friction. Atomistic details, however, are significantly different, including the density and type of TLS. In particular, we find that while the density of TLS involving a bond-hopping mechanism is similar for the two potentials, more complex TLSs, such as those involving a Wooten-Winer-Weaire bond exchange, are about twice as common. Analysis also shows that TLSs, for MTP-based models, are mostly isolated and oscillate independently from each other.