Unveiling Defect Motifs in Amorphous GeSe Using Machine Learning Interatomic Potentials.

Ovonic threshold switching (OTS) selectors are pivotal in nonvolatile memory devices due to their nonlinear electrical characteristics and polarity-dependent threshold voltages. However, the atomic-scale origins of the defect states responsible for these behaviors remain unclear. In this study, we systematically investigate defects in amorphous GeSe using molecular dynamics simulations accelerated by machine learning interatomic potentials (MLIPs). We first benchmark several MLIP architectures, including descriptor-based potentials and graph neural network (GNN)-based potentials. Our results demonstrate that capturing higher-order interactions, at least four-body correlations, and medium-range structural order is essential for accurately representing amorphous GeSe structures. Our analysis indicates that GNN architectures with multiple interaction layers effectively capture higher-order correlations and medium-range order, thereby preventing spurious defects easily introduced by less descriptive MLIPs. Utilizing the optimized GNN model, we identify two distinct defect motifs across 20 independent 960-atom amorphous GeSe structures: aligned Ge chains associated with defect states near the conduction band, and overcoordinated Ge chains linked to defect states near the valence band. Moreover, we establish correlations between electronic defect levels and specific structural features─namely, the average alignment of bond angles in aligned chains and the degree of local Peierls distortion around overcoordinated Ge atoms. These insights provide a theoretical framework for interpreting experimental observations and deepening the understanding of defect-driven OTS phenomena in amorphous GeSe.