BoltzmaNN: Predicting effective pair potentials and equations of state using neural networks

Understand

Neural networks (NNs) are employed to predict equations of state from a given isotropic pair potential using the virial expansion of the pressure.

• The NNs are trained with data from molecular dynamics simulations of monoatomic gases and liquids, sampled in the $NVT$ ensemble at various densities.
• We find that the NNs provide much more accurate results compared to the analytic low-density limit estimate of the second virial coefficient.
• Further, we design and train NNs for computing (effective) pair potentials from radial pair distribution functions, $g(r)$, a task which is often performed for inverse design and coarse-graining.