23-Single-Element-DNPs_RSCDD_2023-Nb

Configurations of Nb 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.

来自Andolina与Saidi2023年发表研究中的铌（Nb）原子构型。本数据集为23组经过精心筛选的极简单元素密度泛函理论（DFT）计算属性数据集之一，用于为深度神经网络势（DNPs）的机器学习建模提供输入数据。每组单元素数据集平均包含约4000个原子结构，每个结构含27个原子。该构型的元数据包含可用的材料项目（Materials Project）ID，以及计算分子动力学（MD）轨迹时的温度参数。对于熔点（MT）低于2000K的元素，所用温度为其熔点与0.25倍熔点；对于熔点高于2000K的元素，所用温度则为其熔点、0.6倍熔点以及0.25倍熔点。