colabfit/DFT_polymorphs_PNAS_2022_PBE_TS_succinic_acid_train

这是一个关于琥珀酸的PBE-TS训练数据集，来源于2022年PNAS上发表的论文Semi-local and hybrid functional DFT data for thermalised snapshots of polymorphs of benzene, succinic acid, and glycine。该数据集使用Quantum Espresso v6.3计算了DFT参考能量和力，采用了半局域PBE交换-相关功能，Tkatchenko-Scheffler色散修正，优化的正则守恒 Vanderbilt伪势， Monkhorst-Pack k点网格和100 Ry的平面波能量截止。数据集中的额外细节存储在以dataset_为前缀的列中。数据集包含29212种独特的分子配置，817936个原子，包括C、H、O元素，属性包括能量、原子力和Cauchy应力。

This is a succinic acid PBE-TS training dataset from the 2022 PNAS publication Semi-local and hybrid functional DFT data for thermalised snapshots of polymorphs of benzene, succinic acid, and glycine. The dataset features DFT reference energies and forces calculated using Quantum Espresso v6.3 with a 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 Å^-1, and a plane-wave energy cutoff of 100 Ry for the wavefunction. Additional details are stored in columns prefixed with dataset_. The dataset contains 29,212 unique molecular configurations, 817,936 atoms, including the elements C, H, O, and properties such as energy, atomic forces, and Cauchy stress.