colabfit/HO_LiMoNiTi_NPJCM_2020_LiMoNiTi_train

HO LiMoNiTi NPJCM 2020 LiMoNiTi训练数据集包含了Li8Mo2Ni7Ti7O32的训练配置，这些配置用于训练一个神经网络，其中通过泰勒展开外推总能量的方法来降低计算成本。数据集中的列以dataset_为前缀，存储了额外的详细信息。该数据集由April M. Cooper, Johannes Kästner, Alexander Urban, Nongnuch Artrith编写，并在NPJ Computational Materials上发表。数据集遵循CC-BY-4.0许可证，包含824个独特的分子配置，共有46144个原子，包含的元素有Li（锂）、Mo（钼）、Ni（镍）、O（氧）、Ti（钛），并且包括能量、原子力和柯西应力等属性。

The HO LiMoNiTi NPJCM 2020 LiMoNiTi train dataset contains training configurations of Li8Mo2Ni7Ti7O32 used in the training of an ANN, where the total energy is extrapolated by a Taylor expansion to reduce computational costs. Additional details are stored in the dataset columns prefixed with dataset_. The dataset is authored by April M. Cooper, Johannes Kästner, Alexander Urban, Nongnuch Artrith and published in NPJ Computational Materials. It is licensed under CC-BY-4.0, includes 824 unique molecular configurations, totals 46144 atoms, comprises elements Li, Mo, Ni, O, Ti, and includes properties such as energy, atomic forces, and Cauchy stress.