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of 66 286
pro vyhledávání: '"Parrinello A"'
Ab-initio molecular dynamics (AIMD) is a powerful tool to simulate physical movements of molecules for investigating properties of materials. While AIMD is successful in some applications, circumventing its high computational costs is imperative to p
Externí odkaz:
http://arxiv.org/abs/2406.18797
Autor:
de Hijes, Pablo Montero, Dellago, Christoph, Jinnouchi, Ryosuke, Schmiedmayer, Bernhard, Kresse, Georg
In this paper we investigate the performance of different machine learning potentials (MLPs) in predicting key thermodynamic properties of water using RPBE+D3. Specifically, we scrutinize kernel-based regression and high-dimensional neural networks t
Externí odkaz:
http://arxiv.org/abs/2312.15213
In this paper, we propose a cost-reduced method for finite-temperature molecular dynamics on a near-term quantum computer, Quantum Car-Parrinello molecular dynamics (QCPMD). One of the most promising applications of near-term quantum computers is qua
Externí odkaz:
http://arxiv.org/abs/2212.11921
Autor:
Montero de Hijes, Pablo1,2 (AUTHOR) pablo.montero.de.hijes@univie.ac.at, Dellago, Christoph1 (AUTHOR), Jinnouchi, Ryosuke3 (AUTHOR), Schmiedmayer, Bernhard1 (AUTHOR), Kresse, Georg1,4 (AUTHOR)
Publikováno v:
Journal of Chemical Physics. 3/21/2024, Vol. 160 Issue 11, p1-12. 12p.
Publikováno v:
Journal of Molecular Liquids 364 (2022) 119936
Water is essential for life and technological applications, mainly for its unique thermodynamic and dynamic properties, often anomalous or counterintuitive. These anomalies result from the hydrogen-bonds fluctuations, as evidenced by studies for supe
Externí odkaz:
http://arxiv.org/abs/2206.05214
First-principles molecular dynamics is employed to describe the atomic structure of amorphous SiN, a non-stoichiometric compound belonging to the Si$_x$N$_{y}$ family. To produce the amorphous state via the cooling of the liquid, both the Car-Parrine
Externí odkaz:
http://arxiv.org/abs/2205.00524
Autor:
van der Heide, Tammo, Kullgren, Jolla, Broqvist, Peter, Bačić, Vladimir, Frauenheim, Thomas, Aradi, Bálint
A new, open source, parallel, stand-alone software package (Fortnet) has been developed, which implements Behler-Parrinello neural networks. It covers the entire workflow from feature generation to the evaluation of generated potentials, coupled with
Externí odkaz:
http://arxiv.org/abs/2202.03118
Autor:
Kývala, Lukáš1,2 (AUTHOR), Dellago, Christoph1 (AUTHOR) Christoph.Dellago@univie.ac.at
Publikováno v:
Journal of Chemical Physics. 9/7/2023, Vol. 159 Issue 9, p1-8. 8p.
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Publikováno v:
In Computer Physics Communications March 2023 284