PIMD data for training effective potential incorporating nuclear quantum effects

The dataset contains training data to generate machine-learned effective potentials reproducing correct nuclear quantum statistics. The generation procedure is described in I. Zaporozhets, F. Musil, V. Kapil, & C. Clementi (2024). Accurate nuclear quantum statistics on machine-learned classical effective potentials. [arXiv: 2407.03448] The code required to generate the dataset can be found in the repository: cg_nuclear_quantum_statistics. Dataset structure The archive contains datasets for four systems: a particle in 1D Morse potential, a single water molecule in a vacuum, a Zundel cation, and a box of 256 water molecules. For the particle in Morse potential, separate .npy files (loadable with numpy in Python) for harmonic coupling (spring) forces and coordinates are given for temperatures 100, 300, and 600 K. For other systems, the data are provided in HDF5 format (see below). Directory structure: CG_quantum_statistics├── 0_morse_potential│ ├── temp_100_spring_forces.npy│ ├── temp_100_total_coordinates.npy│ ├── temp_300_spring_forces.npy│ ├── temp_300_total_coordinates.npy│ ├── temp_600_spring_forces.npy│ └── temp_600_total_coordinates.npy├── 1_h2o_molecule│ └── h2o.h5├── 2_zundel_cation│ └── h5o2+.h5└── 3_bulk_h2o └── h2o_256.h5本数据集包含用于生成可复现准确核量子统计特性的机器学习有效势的训练数据。其生成流程详见I. Zaporozhets、F. Musil、V. Kapil与C. Clementi（2024）发表的论文《机器学习经典有效势下的准确核量子统计》（Accurate nuclear quantum statistics on machine-learned classical effective potentials）[arXiv: 2407.03448]。 用于生成该数据集的代码可在仓库cg_nuclear_quantum_statistics中获取。 数据集结构 本归档文件包含四类体系的数据集：一维莫尔斯势（Morse potential）中的粒子、真空环境下单水分子、尊德阳离子（Zundel cation），以及包含256个水分子的模拟盒子体系。针对莫尔斯势中的粒子，我们提供了分别对应100 K、300 K与600 K温度下的简谐耦合（弹簧）力与坐标的.npy文件（可通过Python的numpy库加载）。针对其余体系，数据以HDF5格式提供（详见下文）。 目录结构： CG_quantum_statistics ├── 0_morse_potential │ ├── temp_100_spring_forces.npy │ ├── temp_100_total_coordinates.npy │ ├── temp_300_spring_forces.npy │ ├── temp_300_total_coordinates.npy │ ├── temp_600_spring_forces.npy │ └── temp_600_total_coordinates.npy ├── 1_h2o_molecule │ └── h2o.h5 ├── 2_zundel_cation │ └── h5o2+.h5 └── 3_bulk_h2o └── h2o_256.h5