Machine Learning-Driven Insights into Excitonic Effects in 2D Materials

TL;DR

This paper presents a machine learning framework that predicts exciton binding energies in 2D materials, significantly speeding up the screening process for materials with strong excitonic effects, and can be extended to 3D systems.

Contribution

Introduces a ML-based method for predicting excitonic properties in 2D materials, offering a faster alternative to traditional computational techniques and enabling efficient materials discovery.

Findings

ML models accurately predict exciton binding energies

Bayesian optimization identifies materials with largest excitonic effects

Framework is adaptable to 3D materials

Abstract

Understanding excitonic effects in two-dimensional (2D) materials is critical for advancing their potential in next-generation electronic and photonic devices. In this study, we introduce a machine learning (ML)-based framework to predict exciton binding energies in 2D materials, offering a computationally efficient alternative to traditional methods such as many-body perturbation theory (GW) and the Bethe-Salpeter equation. Leveraging data from the Computational 2D Materials Database (C2DB), our ML models establish connections between cheaply available material descriptors and complex excitonic properties, significantly accelerating the screening process for materials with pronounced excitonic effects. Additionally, Bayesian optimization with Gaussian process regression was employed to efficiently filter materials with largest exciton binding energies, further enhancing the discovery process. Although developed for 2D systems, this approach is versatile and can be extended to three-dimensional materials, broadening its applicability in materials discovery.