  Using a neural network potential (ANI-1ccx) generated from quantum data on a
large data set of molecules and pairs of molecules, isothermal, constant volume
simulations demonstrate that the model can be as accurate as ab initio
molecular dynamics for simulations of pure liquid water and the aqueous
solvation of a methane molecule. No theoretical or experimental data for the
liquid phase is used to train the model, suggesting that the ANI-1ccx approach
is an effective method to link high level ab initio methods to potentials for
large scale simulations.
