Bingqing Cheng, Edgar A. Engel, JÖrg Behler, Christoph Dellago and Michele Ceriotti

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.

arxiv preprint: https://arxiv.org/abs/1811.08630

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.

• training-set: The training set for machine-learning potentials. Two input formats.

• NN-potential: Parameters for the NN potential. Instructions and examples for running MD using this potential.

• input-files: All the other input files for free energy estimations used in the paper.