23-Single-Element-DNPs RSCDD 2023-Cu

Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.

Cu（铜）的配置，源自Andolina与Saidi于2023年的研究。本配置集为23个简约、精选的DFT（密度泛函理论）计算属性集之一，针对单个元素，旨在为深度神经网络势（DNPs）的机器学习提供输入。每个元素集平均包含约4000个结构，每个结构含有27个原子。配置元数据包括可用的材料项目ID，以及计算MD（分子动力学）轨迹的温度。这些温度对应于熔点（MT）及其0.25倍值，对于熔点低于2000K的元素；而对于熔点高于2000K的元素，则包括熔点、熔点的0.6倍值以及0.25倍值。