用深度学习预测非线性光学材料性能，加速激光材料发现
Graph Neural Network Prediction of Nonlinear Optical Properties
- 基于原子线图神经网络，从材料结构预测非线性光学响应
- 在1 pm/V误差内准确率达82.5%，相对误差不超过0.5%
- 适合材料设计与计算化学领域研究者快速筛选潜在光电器件材料
当前，通过二次谐波生成（SHG）产生激光的非线性光学（NLO）材料备受关注。然而，由于实验方法和第一性原理计算耗时耗资，发现具有显著SHG效应的新材料仍具挑战。本研究提出一种基于原子线图神经网络（ALIGNN）的深度学习方法，利用新型光电材料发现数据库（NOEMD）数据，以Kurtz-Perry（KP）系数为核心目标，构建了可精准预测非线性光学响应的模型。结果表明，该模型在绝对误差不超过1 pm/V的条件下，准确率达到82.5%，相对误差不高于0.5%。本工作展示了深度学习在加速高性能光学材料发现与设计中的潜力。
原文摘要 · Abstract (English)
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.
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