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arXiv 2609.19469physics.chem-ph

基于DFT GGA的H$_2$O势能面、永久矩和极化率张量数据集

DFT GGA based datasets for H$_2$O potential energy surfaces, permanent moment and polarizability tensors

Anoop Ajaya Kumar Nair, Elvar Örn Jónsson

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中文总结 AI 辅助

本文提供了基于GGA泛函的H$_2$O单体、团簇及液态水AIMD轨迹数据集,涵盖势能面、永久矩和极化率张量,用于验证水模型。

中文摘要 AI 辅助

本文提供了H$_2$O分子的势能面、永久矩面(从偶极到十六极)和极化率面(从偶极-偶极到四极-四极)的计算数据,这些数据是在Kohn-Sham密度泛函理论(KS-DFT)的广义梯度近似(GGA)水平下,使用基于网格的投影缀加波代码GPAW和电荷微扰代码C-pol计算得到的。所采用的GGA泛函包括PBE、RPBE和BLYP。单体表面由H$_2$O单体的系统性、对称性约化的形变组成,每个泛函包含10,000个构型,无迹笛卡尔张量矩值以xyz文件格式的字符串形式报告。此外,我们提供了n-H$_2$O聚集体(n=2-6)的团簇数据集,这些数据从类液态构型中采样,并使用相同的KS-DFT设置计算,每个扩展xyz文件在注释行中编码了团簇的总相互作用能。最后,包含了环境条件下含有25至150个H$_2$O分子的周期性液态水盒子的从头算分子动力学(AIMD)轨迹,提供了原子构型和能量的时间序列,这些数据与单体和团簇数据一致,适用于验证经典和机器学习水模型。

英文摘要

We present in this article data on the computed potential energy surface, permanent moment surface (from dipole to hexadecapole) and polarizability surface (from dipole-dipole to quadrupole-quadrupole) for the H$_2$O molecule, calculated at the level of the generalized gradient approximation (GGA) in Kohn-Sham density functional theory (KS-DFT) in the grid-based projector augmented wave code GPAW, using the charge perturbation code C-pol. The GGA-based functionals are PBE, RPBE and BLYP. The monomer surfaces are composed of systematic, symmetry-reduced deformations of the H$_2$O monomer and comprise 10,000 configurations per functional, with traceless Cartesian tensor moment values reported as data strings in the xyz file format. In addition, we provide cluster datasets for n-H$_2$O aggregates (n = 2-6), sampled from liquid-like configurations and computed with the same KS-DFT settings, where each extended xyz file encodes the total interaction energy of the cluster in the comment line. Finally, ab initio molecular dynamics (AIMD) trajectories of periodic liquid water boxes, containing between 25 to 150 H$_2$O molecules at ambient conditions, are included, furnishing time series of atomic configurations and energies that are consistent with the monomer and cluster data and suitable for validating classical and machine-learned water models.

发表机构

  • Science Institute and Faculty of Physical Sciences, University of Iceland(冰岛大学物理科学与科学学院)

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