A Fully Quantum-Mechanical Treatment for Kaolinite
arXiv:2301.04505 · doi:10.1063/5.0152361
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.
12 pages (10 supplementary), 6 figures (10 supplementary)
References in corpus (11)
- A Fourth-Generation High-Dimensional Neural Network Potential with Accurate Electrostatics Including Non-local Charge Transfer
- Accurate sampling using Langevin dynamics
- Efficient stochastic thermostatting of path integral molecular dynamics
- How to remove the spurious resonances from ring polymer molecular dynamics
- Workflows in AiiDA: Engineering a high-throughput, event-based engine for robust and modular computational workflows
- Self-Consistent Determination of Long-Range Electrostatics in Neural Network Potentials
- Ice Formation on Kaolinite: Insights from Molecular Dynamics Simulations
- Toward Accurate Adsorption Energetics on Clay Surfaces
- Transferability of machine learning potentials: Protonated water neural network potential applied to the protonated water hexamer
- Machine learning at the atomic-scale
- Hydrogen Bonding and Nuclear Quantum Effects in Clays