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

Minseok Moon¹, Seungwoo Hwang¹, Jaesun Kim¹

¹Department of Materials Science and Engineering and Research Institute of Advanced Materials, Seoul National University, Seoul 08826, Korea.

ACS Applied Materials & Interfaces

|October 1, 2025

Summary

This summary is machine-generated.

Machine learning potentials accurately model amorphous GeSe, revealing two defect types crucial for Ovonic Threshold Switching (OTS) memory devices. These defects, linked to specific atomic structures, explain the switching behavior in nonvolatile memory.

Keywords:

amorphous GeSe density functional theory electronic structure machine learning interatomic potential midgap defect ovonic threshold switching

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Area of Science:

Materials Science
Computational Materials Science
Solid State Physics

Background:

Ovonic Threshold Switching (OTS) selectors are key components in nonvolatile memory.
Their nonlinear electrical characteristics and polarity-dependent threshold voltages are critical.
The atomic-scale origins of defect states driving OTS remain poorly understood.

Purpose of the Study:

To systematically investigate defect states in amorphous GeSe.
To understand the atomic-scale origins of OTS behavior.
To correlate electronic defect levels with specific structural features.

Main Methods:

Utilized molecular dynamics simulations accelerated by machine learning interatomic potentials (MLIPs).
Benchmarked various MLIP architectures, including descriptor-based and graph neural network (GNN)-based potentials.
Employed an optimized GNN model to analyze 20 independent amorphous GeSe structures.

Main Results:

Identified that higher-order interactions (≥4-body) and medium-range order are essential for accurate amorphous GeSe modeling.
GNN architectures effectively capture these complex interactions, preventing spurious defects.
Discovered two distinct defect motifs: aligned Ge chains (conduction band defects) and overcoordinated Ge chains (valence band defects).
Correlated electronic defect levels with bond angle alignment and Peierls distortion.

Conclusions:

Optimized GNN-based MLIPs provide accurate simulations of amorphous GeSe.
Two specific defect types and their structural origins in amorphous GeSe are identified.
These findings offer a theoretical basis for understanding defect-driven OTS phenomena.