colabfit/DFT_polymorphs_PNAS_2022_PBE_TS_benzene_train

本数据集是苯的PBE-TS训练数据集，来源于文章Semi-local and hybrid functional DFT data for thermalised snapshots of polymorphs of benzene, succinic acid, and glycine。使用Quantum Espresso v6.3软件，采用半局部PBE交换-相关功能、Tkatchenko-Scheffler色散修正、优化的规范保存 Vanderbilt伪势、 Monkhorst-Pack k点网格和100 Ry的平面波能量截止，计算了DFT参考能量和力。数据集中的列以dataset_为前缀，存储了额外的详细信息。数据集包含55000个独特的分子配置，共有1602048个原子，包含的元素有碳(C)和氢(H)，包含的属性有能量、原子力和Cauchy应力。

This dataset is the benzene PBE-TS training dataset from the article Semi-local and hybrid functional DFT data for thermalised snapshots of polymorphs of benzene, succinic acid, and glycine. DFT reference energies and forces were calculated using Quantum Espresso v6.3 with the semi-local PBE exchange-correlation functional, Tkatchenko-Scheffler dispersion correction, optimized norm-conserving Vanderbilt pseudopotentials, a Monkhorst-Pack k-point grid with a maximum spacing of 0.06 x 2π A^-1, and a plane-wave energy cutoff of 100 Ry for the wavefunction. Additional details are stored in dataset columns prefixed with dataset_. The dataset contains 55000 unique molecular configurations, a total of 1602048 atoms, includes elements carbon (C) and hydrogen (H), and properties such as energy, atomic forces, and Cauchy stress.