colabfit/sGDML_Benzene_DFT_NC2018

sGDML Benzene DFT NC2018数据集是由Stefan Chmiela, Huziel E. Sauceda, Klaus-Robert Müller, Alexandre Tkatchenko等人创建的，用于训练DFT模型的训练数据。这些数据是通过在NVT系综下使用 Nose-Hoover恒温器在500K温度下进行200ps的从头分子动力学模拟得到的，时间分辨率为0.5fs。计算力能时使用了广义梯度近似理论和Perdew-Burke-Ernzerhof (PBE)交换-关联功能，用Tkatchenko-Scheffler (TS)方法处理范德华相互作用。所有计算均使用FHI-aims软件完成。最终训练数据是通过在保持最大熵分布的条件下对完整轨迹进行子采样生成的。数据集包含了49863个独特的分子配置和598356个原子，元素包括碳(C)和氢(H)。数据集属性包括能量、原子力和Cauchy应力。

The sGDML Benzene DFT NC2018 dataset, created by Stefan Chmiela, Huziel E. Sauceda, Klaus-Robert Müller, and Alexandre Tkatchenko, is used for training DFT models. The data were generated from ab initio molecular dynamics simulations performed at 500K for 200ps with a 0.5fs time resolution under the NVT ensemble using the Nose-Hoover thermostat. Forces and energies were calculated using the generalized gradient approximation with the Perdew-Burke-Ernzerhof (PBE) exchange-correlation functional and the Tkatchenko-Scheffler (TS) method for van der Waals interactions, with all calculations conducted using FHI-aims. The final training data were produced by subsampling the full trajectory while preserving the Maxwell-Boltzmann distribution for energies. The dataset contains 49863 unique molecular configurations and 598356 atoms, comprising the elements Carbon (C) and Hydrogen (H). The dataset properties include energy, atomic forces, and Cauchy stress.