GPU-Accelerated Neural Network Potential Energy Surfaces for Diffusion Monte Carlo

Diffusion Monte Carlo (DMC) provides
a powerful method for understanding
the vibrational landscape of molecules that are not well-described
by conventional methods. The most computationally demanding step of
these calculations is the evaluation of the potential energy. In this
work, a general approach is developed in which a neural network potential
energy surface is trained by using data generated from a small-scale
DMC calculation. Once trained, the neural network can be evaluated
by using highly parallelizable calls to a graphics processing unit
(GPU). The power of this approach is demonstrated for DMC simulations
on H2O, CH5+, and (H2O)2. The need to include permutation symmetry in the neural network
potentials is explored and incorporated into the molecular descriptors
of CH5+ and (H2O)2. It
is shown that the zero-point energies and wave functions obtained
by using the neural network potentials are nearly identical to the
results obtained when using the potential energy surfaces that were
used to train the neural networks at a substantial savings in the
computational requirements of the simulations.