<div class="csl-bib-body">
<div class="csl-entry">Bučková, N. (2023). <i>Van-der-Waals interactions in neural-network force fields</i> [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2023.102444</div>
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dc.identifier.uri
https://doi.org/10.34726/hss.2023.102444
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/188835
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dc.description
Abweichender Titel nach Übersetzung der Verfasserin/des Verfassers
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dc.description.abstract
In recent years, new methods based on machine-learning techniques and neural-networks have been developed to calculate the thermodynamic and spectroscopic properties of various systems with high precision and low computational costs. However, most current neural-network force fields have a significant limitation: they only take into account local, short-range interactions between atoms and molecules, while largely ignoring the longrange interactions that may strongly influence the properties of materials. This limitation may have significant consequences for predicting the physical properties of materials that are primarily determined by long-range interactions. This master thesis discusses the extent to which long-range interactions are taken into account in the current neuralnetwork force field NeuralIL, and how this limitation affects the accuracy of predicted density using molecular dynamics simulations for water as a case study. Moreover a new implementation of the DFT-D3 method for calculating dispersion interactions using the high-performance machine-learning framework JAX will be presented. Finally, the suitability of vdW-DF exchange-correlation functionals to correct for those effects in condensed-phase water systems will be analysed and compared to DFT calculations with RPBE functional with and without the DFT-D3 dispersion correction.
en
dc.language
English
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dc.language.iso
en
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dc.rights.uri
http://rightsstatements.org/vocab/InC/1.0/
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dc.subject
Machine Learning
de
dc.subject
Wasser
de
dc.subject
Van-der-Waals
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dc.subject
Machine Learning
en
dc.subject
Water
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dc.subject
Van-der-Waals
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dc.title
Van-der-Waals interactions in neural-network force fields
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dc.title.alternative
Van-der-Waals-Wechselwirkungen in Kraftfeldern neuronaler Netzwerke