Accelerated materials discovery of complex multicomponent alloys and ceramics with deep reinforcement learning

In recent years  the study of multicomponent alloys and ceramics including disordered rock salt cathodes for Li-ion batteries, metal halide perovskite solar absorbers, alloys for thermoelectrics, ultra-high-temperature ceramics for aerospace applications has seen rapid growth due to their superior mechanical, thermal, and magnetic properties. The immense configurational space offers unique opportunities for discovery of new materials with unprecedented qualities. However, discovery of materials with targeted properties is challenging due to  the huge number of distinct atomic configurations available  . We employ multidimensional scaling (MDS) strategies to reduce the size of the configurational manifold.  We use the distribution of local environments of elements in a given configuration as descriptors of the configuration.  The atomic environments are described  using features such as bond length and order parameter.  The dimensionality reduction is beneficial for the reinforcement learning (RL) approach, which scales poorly for large numbers of space states. We compare the number of exploration steps needed to reach the target configuration before training (random walk) and after training the deep RL model. We demonstrate the approach to discover configurations for ternary compounds such as (Al_xGa_yIn_z)_2NO_3N and high-entropy ceramics. Deep RL is implemented on the reduced 3D and 2D manifold in order to achieve target physical property.