Multi-property directed generative design of inorganic materials through Wyckoff-augmented transfer learning

Understand

Accelerated materials discovery is an urgent demand to drive advancements in fields such as energy conversion, storage, and catalysis.

• Property-directed generative design has emerged as a transformative approach for rapidly discovering new functional inorganic materials with multiple desired properties within vast and complex search spaces.
• However, this approach faces two primary challenges: data scarcity for functional properties and the multi-objective optimization required to balance competing tasks.
• Here, we present a multi-property-directed generative framework designed to overcome these limitations and enhance site symmetry-compliant crystal generation beyond P1 (translational) symmetry.