Research data for 'How Realistic are Idealized Copper Surfaces? A Machine Learning Study of Rough Copper-Water Interfaces'

This dataset supports the paper 'How Realistic are Idealized Copper Surfaces? A Machine Learning Study of Rough Copper-Water Interfaces'. The paper can be found under the following doi: https://doi.org/10.1021/acsmaterialsau.5c00174 The following files are provided: Database.zip: Database containing the structures used for training and testing together with the DFT energy and force labels. RoughSurfaces.zip: Contains all 46 rough copper structures including water on top. ActiveLearning.zip: Contains a python script, which was used to extract high uncertainty surface environments into smaller boxes and the corresponding lammps input file to perform the MD. Potentials.zip: Contains all potentials, which have been used in this work. UMAP.zip: Contains the coordinates of all the points in the UMAP figure. TrajectoryMD.zip: Contains the exemplary trajectory of one of the rough copper-water interfaces, which was also used to generate the UMAP data.本数据集配套论文《理想化铜表面的真实程度？粗糙铜-水界面的机器学习研究》，该论文可通过以下DOI获取：https://doi.org/10.1021/acsmaterialsau.5c00174 本次提供的文件如下： Database.zip：包含用于训练与测试的结构数据集，以及密度泛函理论（DFT）能量与力标签。 RoughSurfaces.zip：包含全部46个粗糙铜结构，每个结构均附带表面吸附的水分子。 ActiveLearning.zip：包含一份Python脚本，该脚本用于将高不确定性的表面环境提取至小型盒子中，以及对应的LAMMPS输入文件以执行分子动力学（MD）模拟。 Potentials.zip：包含本研究中使用的全部势函数。 UMAP.zip：包含均匀流形近似与投影（UMAP）图中所有数据点的坐标。 TrajectoryMD.zip：包含一个粗糙铜-水界面的示例性分子动力学轨迹，该轨迹同样用于生成UMAP数据。