A practical inverse design approach for high-entropy catalysts using generative AI

The vast compositional space of high-entropy materials offers exceptional opportunities for the development of powerful catalysts. However, the inverse design of these materials remains unfeasible due to the lack of robust theoretical frameworks and high-throughput experimental tools. This study demonstrates a practical inverse design approach that integrates spectroscopic descriptors, generative machine learning, and a robotic experimental platform to synthesize and optimize catalysts for the oxygen evolution reaction. The automated system substantially accelerated catalyst design and experimental validation, reducing the time required for synthesis, characterization and performance testing from approximately 20 h to only 78 min per sample. Following a rapid screen for efficient senary high-entropy catalysts, the spectroscopic generative model further optimized the top-performing candidate, lowering its overpotential at 10 mA cm −2 by an additional 32.0 mV. Our findings demonstrate the potential of an inverse design approach that incorporates spectroscopic descriptors into generative machine learning to accelerate catalyst discovery. Moreover, this approach is expected to drive the intelligent design of high-performance complex materials.