Graph Neural Network Prediction of Nonlinear Optical Properties
This work addresses the problem of time-consuming and costly material discovery for researchers in optics and materials science, representing an incremental improvement by applying an existing deep learning method to a specific domain.
The study tackled the challenge of discovering nonlinear optical materials for second harmonic generation by developing a deep learning model using the Atomistic Line Graph Neural Network (ALIGNN) to predict these properties, achieving 82.5% accuracy at a tolerated absolute error up to 1 pm/V and relative error not exceeding 0.5.
Nonlinear optical (NLO) materials for generating lasers via second harmonic generation (SHG) are highly sought in today's technology. However, discovering novel materials with considerable SHG is challenging due to the time-consuming and costly nature of both experimental methods and first-principles calculations. In this study, we present a deep learning approach using the Atomistic Line Graph Neural Network (ALIGNN) to predict NLO properties. Sourcing data from the Novel Opto-Electronic Materials Discovery (NOEMD) database and using the Kurtz-Perry (KP) coefficient as the key target, we developed a robust model capable of accurately estimating nonlinear optical responses. Our results demonstrate that the model achieves 82.5% accuracy at a tolerated absolute error up to 1 pm/V and relative error not exceeding 0.5. This work highlights the potential of deep learning in accelerating the discovery and design of advanced optical materials with desired properties.