  The presented work demonstrates the training of recurrent neural networks
(RNNs) from distributions of atom coordinates in solid state structures that
were obtained using ab initio molecular dynamics (AIMD) simulations. AIMD
simulations on solid state structures are treated as a multi-variate
time-series problem. By referring interactions between atoms over the
simulation time to temporary correlations among them, RNNs find patterns in the
multi-variate time-dependent data, which enable forecasting trajectory paths
and potential energy profiles. Two types of RNNs, namely gated recurrent unit
and long short-term memory networks, are considered. The model is described and
compared against a baseline AIMD simulation on an iridium oxide slab. Findings
demonstrate that both networks can potentially be harnessed for accelerated
statistical sampling in computational materials research.
