用局部坐标系的图网络直接预测分子极化率，更准确。
Direct Molecular Polarizability Prediction with SO(3) Equivariant Local Frame GNNs
- 基于局部坐标系设计等变图网络，同时处理标量、矢量和张量信息。
- 在QM7-X数据集上，张量消息传递模型比标量模型性能更优。
- 适合需要几何感知的分子性质预测研究者使用。
我们提出一种新型等变图神经网络架构，用于直接预测分子的张量响应性质。与传统方法先回归标量再求导得到张量不同，本方法通过局部坐标系保持$SO(3)$等变性。模型在局部消息传递框架中整合标量、矢量和张量通道，有效捕捉分子几何信息。我们在QM7-X数据集上评估模型性能，结果表明张量消息传递优于标量消息传递模型。该工作推动了结构化、几何感知神经模型在分子性质预测中的发展。
原文摘要 · Abstract (English)
We introduce a novel equivariant graph neural network (GNN) architecture designed to predict the tensorial response properties of molecules. Unlike traditional frameworks that focus on regressing scalar quantities and derive tensorial properties from their derivatives, our approach maintains $SO(3)$-equivariance through the use of local coordinate frames. Our GNN effectively captures geometric information by integrating scalar, vector, and tensor channels within a local message-passing framework. To assess the accuracy of our model, we apply it to predict the polarizabilities of molecules in the QM7-X dataset and show that tensorial message passing outperforms scalar message passing models. This work marks an advancement towards developing structured, geometry-aware neural models for molecular property prediction.
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