colabfit/sGDML_Benzene_ccsdt_NC2018_train

sGDML苯ccsdt NC2018训练数据集是来自sGDML的苯数据集的训练集。该数据集是通过在NVT系综下使用 Nose-Hoover恒温器在500 K温度下进行200 ps模拟，并使用0.5 fs的时间分辨率来训练模型的。使用全电子耦合簇单、双激发和微扰三激发（CCSD(T)）方法重新计算了能量和力。对于苯，使用了Dunning相关一致基组cc-pVDZ。所有计算都是使用Psi4软件套件完成的。数据集中的额外详细信息存储在以dataset_为前缀的列中。数据集包含1000个独特的分子配置和12000个原子，包括碳(C)和氢(H)元素，以及能量、原子力和柯西应力等属性。

The sGDML Benzene ccsdt NC2018 train dataset is the training set from the benzene dataset of sGDML. The dataset for training the models was created by running ab initio MD in the NVT ensemble using the Nosé-Hoover thermostat at 500 K for a 200 ps simulation with a time resolution of 0.5 fs. Energies and forces were recalculated using all-electron coupled cluster with single, double, and perturbative triple excitations (CCSD(T)). The Dunning correlation-consistent basis set cc-pVDZ was used for benzene. All calculations were performed with the Psi4 software suite. Additional details are stored in columns prefixed with dataset_. The dataset contains 1000 unique molecular configurations and 12000 atoms, including the elements Carbon (C) and Hydrogen (H), and properties such as energy, atomic forces, and Cauchy stress.