Materials Informatics Framework for Accelerated Discovery of High-Refractive-Index 2D Materials.

ABSTRACT: Accurate and efficient prediction of the optical properties of two-dimensional (2D) materials is crucial for photonic applications but remains a challenging task due to discrepancies between theoretical and experimental approaches. Here, we present a physics-guided machine learning (ML) framework for accelerated screening of 2D materials. It combines first-principles density functional theory (DFT) calculations and graph neural network models backed by experimental spectroscopic validation and Cauchy-model integration. Within this framework, we collected a database of more than 1000 monolayers of transition metal dichalcogenides (TMDs) along with their optical properties. We also proposed a universal method for defining a physically meaningful thickness of two-dimensional structures, which

SUBMITTER: Klimova LA 

PROVIDER: S-EPMC12854759 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature