eQM7: a dataset for small molecules with Foster-Boys centers

The electron QM7 (eQM7) dataset is created with the purpose of training and validating polarizable (machine learning) force fields on non-equilibrium configurations of small molecules. It contains 6868 molecules with hydrogen, carbon, nitrogen and oxygen. For each molecule, 500 perturbations are constructed using normal mode sampling, torsion sampling, dimer sampling and homogeneous electric fields. Energies, forces and Foster-Boys centers are computed using density functional theory (DFT) with the PBE0 functional, Aug-cc-pVTZ basis set in the ab-initio quantum chemistry code Psi4.

电子QM7（eQM7）数据集旨在针对小分子非平衡构型训练并验证可极化（机器学习）力场。该数据集涵盖6868种由氢、碳、氮与氧组成的分子。针对每个分子，通过简正模采样、扭转采样、二聚体采样以及均匀电场调控四种方式构建500组构型扰动。其能量、原子受力与福斯特-博伊斯中心（Foster-Boys centers）均通过从头算量子化学程序Psi4，采用搭载PBE0泛函与Aug-cc-pVTZ基组的密度泛函理论（DFT）计算得到。

搜集汇总

背景与挑战

背景概述

eQM7数据集是一个包含6868个小分子的数据集，主要用于极化力场的训练和验证。每个分子有500种扰动构型，数据通过PBE0泛函和Aug-cc-pVTZ基组的密度泛函理论计算获得，包含能量、力和Foster-Boys中心等关键信息。