Abstract
Neural network potentials for kaolinite minerals have been fitted to data extracted from density functional theory calculation that were performed using the revPBE + D3 and revPBE + vdW functionals. These potentials have then been used to calculate static and dynamic properties of the mineral. We show that revPBE + vdW is better at reproducing the static properties. However, revPBE + D3 does a better job of reproducing the experimental IR spectrum. We also consider what happens to these properties when a fully-quantum treatment of the nuclei is employed. We find that nuclear quantum effects (NQEs) do not make a substantial difference to the static properties. However, when NQEs are included the dynamic properties of the material change substantially.
Original language | English |
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Article number | 204704 |
Journal | Journal of Chemical Physics |
Volume | 158 |
Issue number | 20 |
DOIs | |
Publication status | Published - 28 May 2023 |
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Dive into the research topics of 'A fully quantum-mechanical treatment for kaolinite'. Together they form a unique fingerprint.Datasets
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Clay Neural Network (ClayNN) Potentials
Shepherd, S. (Creator), Queen's University Belfast, 2023
https://github.com/sshepherd637/ClayNN
Dataset
Student theses
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Neural network interatomic potentials for kaolin minerals
Shepherd, S. R. (Author), Wilkins, D. (Supervisor) & Tribello, G. (Supervisor), Dec 2024Student thesis: Doctoral Thesis › Doctor of Philosophy
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