发表机构
Karlsruhe Institute of Technology (KIT)（卡尔斯鲁厄理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出等变图神经网络直接从分子几何和紫外-可见光谱预测动态极化率张量，在QM9和有机光伏数据集上验证，器件模拟结果与TD-DFT高度一致（R²=0.94），加速有机分子筛选。
AI 中文摘要
预测有机分子的动态极化率张量对于模拟光电器件中的光-物质相互作用至关重要，然而传统量子化学方法如含时密度泛函理论（TD-DFT）计算成本高昂，限制了大规模筛选。我们提出了一种等变图神经网络架构，可直接从易于获取的分子信息（如三维几何结构和紫外-可见光谱）预测频率依赖的复值动态极化率张量。该模型学习近似动态极化率张量的形状和大小，捕捉定义分子光学响应的色散和吸收特征。我们使用多种指标（包括适用于比较光谱量的推土机距离）在QM9数据集和哈佛有机光伏数据集中的分子上评估了该模型。对于两个数据集，模型均成功再现了动态极化率张量的主要特征，并能泛化到多种不同分子。为评估适用性，我们进一步通过下游工作流验证了模型，用于计算有机光伏器件的光学性质。我们使用模型预测的极化率进行器件级光学模拟，所得电荷载流子生成速率与基于TD-DFT的结果一致（$R^2 = 0.94$）。我们的方法为传统方法（如TD-DFT）提供了一种有前景的补充甚至替代方案，能够更快、更高效地筛选用于光电器件应用的光活性有机分子。
英文摘要
Predicting the dynamic polarizability tensor of organic molecules is essential for simulating light-matter interactions in optoelectronic devices, yet conventional quantum-chemical methods such as time-dependent density functional theory (TD-DFT) are computationally expensive and limit large-scale screening. We present an equivariant graph neural network architecture that predicts frequency-dependent, complex-valued dynamic polarizability tensors directly from readily available molecular information, such as 3D geometry and UV-vis spectra. The model learns to approximate the shape and magnitude of the dynamic polarizability tensor, capturing both dispersive and absorptive features that define the molecular optical response. We evaluate the model on molecules from both the QM9 dataset and the Harvard organic photovoltaic dataset using various metrics, including the earth mover's distance, which is suitable for comparing spectral quantities. For both datasets, the model successfully reproduces the main features of the dynamic polarizability tensor and generalizes across diverse molecules. To assess the applicability, we further validated the model using a downstream workflow to calculate the optical properties of organic photovoltaic devices. We used the model-predicted polarizabilities to perform device-level optical simulations, yielding charge carrier generation rates in agreement with TD-DFT-based results ($R^2 = 0.94$). Our approach offers a promising complement, or even an alternative, to conventional methods such as TD-DFT, enabling faster and more efficient screening of photoactive organic molecules for optoelectronic applications.
