Excitonic Landscapes in Monolayer Lateral Heterostructures Revealed by Unsupervised Machine Learning

TL;DR

This paper presents an unsupervised machine learning framework that efficiently analyzes hyperspectral photoluminescence data to reveal excitonic landscapes and domain variations in 2D lateral heterostructures, advancing understanding of their optoelectronic properties.

Contribution

The authors develop a scalable ML approach combining PCA, t-SNE, and DBSCAN to interpret complex optical datasets of 2D heterostructures, enabling automated identification of spectral domains and emission species.

Findings

Uncovered spectrally distinct domains related to composition and strain.

Identified multiple emission species including excitons and defect states.

Demonstrated robust, automated analysis of large hyperspectral datasets.

Abstract

Two-dimensional (2D) in-plane heterostructures including compositionally graded alloys and lateral heterostructures with defined interfaces display rich optoelectronic properties and offer versatile platforms to explore one-dimensional interface physics and many-body interaction effects. Graded \(\mathrm{Mo}_x\mathrm{W}_{1-x}\mathrm{S}_2\) alloys show smooth spatial variations in composition and strain that continuously tune excitonic emission, while \(\mathrm{MoS}_2\)--\(\mathrm{WS}_2\) lateral heterostructures contain atomically sharp interfaces supporting one-dimensional excitonic phenomena. These single-layer systems combine tunable optical and electronic properties with potential for stable, high-performance optoelectronic devices. Hyperspectral and nano-resolved photoluminescence (PL) imaging enable spatial mapping of optical features along with local variations in composition, strain, and defects, but manual interpretation of such large datasets is slow and subjective. Here, we introduce a fast and scalable unsupervised machine-learning (ML) framework to extract quantitative and interpretable information from hyperspectral PL datasets of graded \(\mathrm{Mo}_x\mathrm{W}_{1-x}\mathrm{S}_2\) alloys and \(\mathrm{MoS}_2\)--\(\mathrm{WS}_2\) heterostructures. Combining principal-component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), and density-based spatial clustering (DBSCAN), we uncover spectrally distinct domains associated with composition, strain, and defect variations. Decomposition of representative spectra reveals multiple emission species, including band-edge excitons and defect-related transitions, demonstrating that ML-driven analysis provides a robust and automated route to interpret rich optical properties of 2D materials.