DFT GGA based datasets for H2O potential energy surfaces, permanent moment and polarizability tensors
Anoop Ajaya Kumar Nair, Elvar Örn Jónsson
Abstract
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 H2O 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 H2O 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-H2O 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 H2O 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.
Create a lesson
Related papers
Molecular Geometry Understanding Has Unintendedly Emerged in Frontier Large Language Models
Gregorii A. Semakin, Timofey V. Losev, Ilya V. Prolomov et al.
Truncated automatic sparse differentiation for machine learning interatomic potentials
Marcel F. Langer, Adrian Hill, Michele Ceriotti
Collective Ion Dynamics from Finite-Volume Fluctuations in Model Explicit-Solvent Electrolytes
Jeongmin Kim
FOSY: Segmental Backbone Assignment in Intrinsically Disordered Proteins
Dmitry M. Lesovoy, Tatiana Agback, Panagiota S. Georgoulia et al.
Efficient tensorized evaluation of permutation invariant polynomials for representing potential energy surfaces
Junhong Li, Kaisheng Song, Hua Guo et al.
Agentic AI for Density-Functional Development: Revisiting r2SCAN
Santosh Adhikari, Kelsey A. Parker, Etinosa Osaro et al.