  The design of materials structure for optimizing functional properties and
potentially, the discovery of novel behaviors is a keystone problem in
materials science. In many cases microstructural models underpinning materials
functionality are available and well understood. However, optimization of
average properties via microstructural engineering often leads to
combinatorically intractable problems. Here, we explore the use of the
reinforcement learning (RL) for microstructure optimization targeting the
discovery of the physical mechanisms behind enhanced functionalities. We
illustrate that RL can provide insights into the mechanisms driving properties
of interest in a 2D discrete Landau ferroelectrics simulator. Intriguingly, we
find that non-trivial phenomena emerge if the rewards are assigned to favor
physically impossible tasks, which we illustrate through rewarding RL agents to
rotate polarization vectors to energetically unfavorable positions. We further
find that strategies to induce polarization curl can be non-intuitive, based on
analysis of learned agent policies. This study suggests that RL is a promising
machine learning method for material design optimization tasks, and for better
understanding the dynamics of microstructural simulations.
