构建14种元素小分子光谱数据集，助力机器学习模拟更精准
QMe14S, A Comprehensive and Efficient Spectral Dataset for Small Organic Molecules
- 基于DFT计算18.6万个小分子几何与光谱性质
- 包含47类官能团，覆盖14种元素的动态与静态属性
- 新模型在光谱预测上超越旧数据集，适合分子模拟研究
构建了包含186,102个小型有机分子的QMe14S数据集，涵盖H、B、C、N、O、F、Al、Si、P、S、Cl、As、Se、Br共14种元素及47类官能团。采用密度泛函理论（B3LYP/TZVP）优化几何结构，并计算能量、原子电荷、原子力、偶极矩、四极矩、极化率、八极矩、一阶超极化率和赫森矩阵等性质。在同一级别下获取了谐波红外、拉曼和NMR光谱。此外，通过从头算分子动力学生成动态构型，提取非平衡态性质如能量、力和赫森矩阵。利用E(3)-等变消息传递神经网络（DetaNet），验证了在QMe14S上训练的模型在分子光谱模拟中优于以往的QM9S数据集。该数据集为分子模拟提供全面基准，揭示结构-性质关系。
原文摘要 · Abstract (English)
Developing machine learning protocols for molecular simulations requires comprehensive and efficient datasets. Here we introduce the QMe14S dataset, comprising 186,102 small organic molecules featuring 14 elements (H, B, C, N, O, F, Al, Si, P, S, Cl, As, Se, Br) and 47 functional groups. Using density functional theory at the B3LYP/TZVP level, we optimized the geometries and calculated properties including energy, atomic charge, atomic force, dipole moment, quadrupole moment, polarizability, octupole moment, first hyperpolarizability, and Hessian. At the same level, we obtained the harmonic IR, Raman and NMR spectra. Furthermore, we conducted ab initio molecular dynamics simulations to generate dynamic configurations and extract nonequilibrium properties, including energy, forces, and Hessians. By leveraging our E(3)-equivariant message-passing neural network (DetaNet), we demonstrated that models trained on QMe14S outperform those trained on the previously developed QM9S dataset in simulating molecular spectra. The QMe14S dataset thus serves as a comprehensive benchmark for molecular simulations, offering valuable insights into structure-property relationships.
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