Graph Neural Network Prediction of Nonlinear Optical Properties

TL;DR

This paper introduces a deep learning model based on the ALIGNN architecture to predict nonlinear optical properties of materials, significantly speeding up the discovery process for new NLO materials.

Contribution

The study presents a novel application of the Atomistic Line Graph Neural Network (ALIGNN) for predicting NLO properties, achieving high accuracy and reducing reliance on costly experimental and computational methods.

Findings

Model achieves 82.5% accuracy within 1 pm/V error margin

Uses NOEMD database and Kurtz-Perry coefficient as key targets

Demonstrates deep learning's potential in optical material discovery

Abstract

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.