Publication date: Dec 04, 2018
Thermodynamic properties of liquid water as well as hexagonal (Ih) and cubic (Ic) ice are predicted based on density functional theory at the hybrid-functional level, rigorously taking into account quantum nuclear motion, anharmonic fluctuations and proton disorder. This is made possible by combining advanced free energy methods and state-of-the-art machine learning techniques. The ab initio description leads to structural properties in excellent agreement with experiments, and reliable estimates of the melting points of light and heavy water. We observe that nuclear quantum effects contribute a crucial 0.2 meV/H2O to the stability of ice Ih, making it more stable than ice Ic. Our computational approach is general and transferable, providing a comprehensive framework for quantitative predictions of ab initio thermodynamic properties using machine learning potentials as an intermediate step. In this set of supplemental materials, we have included the neural network potential for bulk water, including its training set in two different formats. We have also included the input files for running free energy calculations.
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|478 Bytes||An overview of the data set.|
|22.2 KiB||The parameters of the water neural network potential based on revPBE0-D3 DFT, and an example on how to use it.|
|7.4 MiB||A whole set of input files for running * path-integral molecular dynamics simulations ./pimd/ * Free energy estimation of an ice system using thermodynamic integration method using the NN potential ./NN-TI/ * revPBE0-D3 DFT calculations using the CP2K code ./cp2k-input/ * compute the chemical potential difference between ice and liquid water using the interface pinning method ./interface-pinning/ * Thermodynamic integration between the MBPOL water potential and the neural network potential ./mbpol-TI/ * a sample python data analysis notebook ./data-analysis/|
|17.4 MiB||The training set for ML potentials, based on revPBE0-D3 DFT. 1593 bulk liquid water configurations + energy + forces * input.data: the format for training neural network potentials. * dataset_1593.xyz: in libatom format.|