23-Single-Element-DNPs RSCDD 2023-Sb

Configurations of Sb 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 年所配置的 Sb（锑）数据集配置，系 23 个经过精心挑选的、以 DFT（密度泛函理论）计算得出的单一元素性质的最简数据集之一，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，每个结构由 27 个原子组成。配置元数据包括可用的 Materials Project ID，以及用于计算 MD（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；而对于熔点高于 2000K 的元素，则包括熔点、熔点的 0.6 倍以及熔点的 0.25 倍。