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Dataset . 2020
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2020
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2020
License: CC BY
Data sources: ZENODO
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N-ASW: Molecular Dynamics Data

Authors: Germán Molpeceres; Viktor Zaverkin; Johannes Kästner;

N-ASW: Molecular Dynamics Data

Abstract

Description The data set was generated from ab-initio molecular dynamics simulations and was used to study the adsorption and desorption dynamics of nitrogen atoms on top of amorphous solid water (ASW) [1]. The data set contains structures with 3 to 378 atoms which result in 28,715 structures in total. The atomization energies and atomic forces are calculated at the PBEh-3c/def2-mSVP level of theory [2]. For more details, see Ref. 1. Format The data is stored in python compressed array format (.npz) with the atomization energy in kcal/mol and atomic forces in kcal/mol/Ang. The data set contains five numpy arrays import numpy as np data = np.load('N-ASW.npz') data['R'] # Cartesian coordinates of nuclei (Ang.) data['E'] # Total energy (kcal/mol) data['F'] # Atomic forces (kcal/mol/Ang.) data['N'] # Number of atoms in each structure data['Z'] # Nuclear charges References [1] Molpeceres G.; Zaverkin V.; and Kästner J. Neural-Network Assisted Study of Nitrogen Atom Dynamics on Amorphous Solid Water. I. Adsorption & Desorption. Mon. Not. R. Astron. Soc. 2020, submitted [2] Grimme S.; Brandenburg J. G.; Bannwarth C.; Hansen A. Consistent structures and interactions by density functional theory with small atomic orbital basis sets. J. Chem. Phys. 2015, 143, 054107.

Related Organizations
Keywords

Astrochemistry, Machine Learning, Molecular Dynamics, Density Functional Theory

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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