  A major obstacle to the realization of novel inorganic materials with
desirable properties is the inability to perform efficient optimization across
both materials properties and synthesis of those materials. In this work, we
propose a reinforcement learning (RL) approach to inverse inorganic materials
design, which can identify promising compounds with specified properties and
synthesizability constraints. Our model learns chemical guidelines such as
charge and electronegativity neutrality while maintaining chemical diversity
and uniqueness. We demonstrate a multi-objective RL approach, which can
generate novel compounds with targeted materials properties including formation
energy and bulk/shear modulus alongside a lower sintering temperature synthesis
objectives. Using this approach, the model can predict promising compounds of
interest, while suggesting an optimized chemical design space for inorganic
materials discovery.
