23-Single-Element-DNPs RSCDD 2023-Al

Configurations of Al 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.

Andolina 与 Saidi 于 2023 年发布的 AI 配置数据集，该数据集由 23 组简约化、精心挑选的、基于密度泛函理论计算的单一元素性质构成，旨在为深度神经网络势能（DNPs）的机器学习提供输入。每个元素集平均包含约 4000 个结构，每个结构包含 27 个原子。配置元数据包括可用的材料项目 ID，以及计算分子动力学轨迹的温度。这些温度对应于熔点（MT）以及熔点的 0.25 倍对于熔点低于 2000K 的元素，以及熔点、熔点的 0.6 倍和熔点的 0.25 倍对于熔点高于 2000K 的元素。