A Practical Inverse Design Approach for High-Entropy Catalysts with Generative AI

The vast compositional space of high-entropy materials offers unprecedented opportunities for the development of powerful catalysts. However, their inverse design 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 catalyst composition for the oxygen evolution reaction (OER). The automated system significantly accelerated catalysts design and experimental validation, reducing the time required for synthesis, characterization and performance testing from approximately 20 hours to only 78 minutes 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 mV. Our findings are a testament to the potential of an inverse design approach that incorporates spectroscopic descriptors into generative machine learning to accelerate catalyst discovery. Moreover, this approach is also expected to drive the intelligent design of high-performance complex materials.