Using Neural Network Force Fields to Ascertain the Quality of Ab Initio Simulations of Liquid Water.
Alberto TorresLuana S PedrozaMarivi Fernandez-SerraAlexandre Reily RochaPublished in: The journal of physical chemistry. B (2021)
Accurately simulating the properties of bulk water, despite the apparent simplicity of the molecule, is still a challenge. In order to fully understand and reproduce its complex phase diagram, it is necessary to perform simulations at the ab initio level, including quantum mechanical effects both for electrons and nuclei. This comes at a high computational cost, given that the structural and dynamical properties tend to require long timescales and large simulation cells. In this work, we evaluate the errors that density functional theory (DFT)-based simulations routinely incur into due time- and size-scale limitations. These errors are evaluated using neural-network-trained force fields that are accurate at the level of DFT methods. We compare different exchange and correlation potentials for properties of bulk water that require large timescales. We show that structural properties are less dependent on the system size and that dynamical properties such as the diffusion coefficient have a strong dependence on the simulation size and timescale. Our results facilitate comparisons of DFT-based simulation results with experiments and offer a path to discriminate between model and convergence errors in these simulations.
Keyphrases
- density functional theory
- molecular dynamics
- neural network
- monte carlo
- patient safety
- adverse drug
- cell proliferation
- magnetic resonance
- molecular docking
- computed tomography
- emergency department
- magnetic resonance imaging
- diffusion weighted imaging
- crystal structure
- molecular dynamics simulations
- cell death
- endoplasmic reticulum stress
- resistance training