Enhanced sampling of Crystal Nucleation with Graph Representation Learnt
  Variables
Ziyue Zou Pratyush Tiwary
In this study, we present a graph neural network-based learning approach
using an autoencoder setup to derive low-dimensional variables from features
observed in experimental crystal structures. These variables are then biased in
enhanced sampling to observe state-to-state transitions and reliable
thermodynamic weights. Our approach uses simple convolution and pooling
methods. To verify the effectiveness of our protocol, we examined the
nucleation of various allotropes and polymorphs of iron and glycine from their
molten states. Our graph latent variables when biased in well-tempered
metadynamics consistently show transitions between states and achieve accurate
free energy calculations in agreement with experiments, both of which are
indicators of dependable sampling. This underscores the strength and promise of
our graph neural net variables for improved sampling. The protocol shown here
should be applicable for other systems and with other sampling methods.