QM9S dataset

We constructed the QM9Spectra(QM9S) dataset using 130K organic molecules based on the popular QM9 dataset. We firstly re-optimized molecular geometries using the Gaussian16 package (B.01 version) at B3LYP/def-TZVP level of theory. Then the molecular properties including scalars (energy, NPA charges, etc.), vectors (electric dipole, etc.), 2nd order tensors (Hessian matrix, quadrupole moment, polarizability, etc.), and 3rd order tensors (octupole moment, first hyperpolarizability, etc.) were calculated at the same level. The frequency analysis and time-dependent density functional theory (TD-DFT) were carried out at the same level to obtain the infrared, Raman, and UV-Vis spectra.Two versions of the dataset, .pt (torch_geometric version) and .csv, are provided for training and use. In addition, we also provide broadened spectra.When using this dataset, please cite to the original article's doi: https://doi.org/10.1038/s43588-023-00550-y instead of the doi provided by figshare.本研究基于广受欢迎的QM9数据集，利用13万个有机分子构建了QM9光谱（QM9S）数据集。我们首先使用Gaussian16软件包（B.01版本），在B3LYP/def-TZVP理论级别下对分子几何结构进行了重新优化。随后在同一理论级别下，计算了包括标量（能量、自然布居分析（NPA）电荷等）、矢量（电偶极矩等）、二阶张量（海森矩阵、四极矩、极化率等）以及三阶张量（八极矩、一阶超极化率等）在内的分子性质。随后还在该理论级别下开展频率分析与含时密度泛函理论（Time-Dependent Density Functional Theory, TD-DFT）计算，以获取红外光谱、拉曼光谱以及紫外-可见（UV-Vis）光谱。本数据集提供两种格式版本：.pt格式（适配torch_geometric框架）与.csv格式，可用于模型训练与后续使用。此外，本数据集还附带展宽后的光谱数据。使用本数据集时，请引用原文的DOI：https://doi.org/10.1038/s43588-023-00550-y，而非figshare提供的DOI。

搜集汇总

背景与挑战

背景概述

QM9S数据集是基于QM9数据集构建的，包含130K有机分子的光谱和多种分子属性数据，适用于深度学习和光谱分析研究。数据集提供了.pt和.csv两种格式，方便不同需求的使用者。