Inverse design of promising electrocatalysts for CO2 reduction via generative models and bird swarm algorithm

Directly generating material structures with optimal properties is a long-standing goal in material design. Traditional generative models often struggle to efficiently explore the global chemical space, limiting their utility to localized space. Here, we present a framework named Material Generation with Efficient Global Chemical Space Search (MAGECS) that addresses this challenge by integrating the bird swarm algorithm and supervised graph neural networks, enabling effective navigation of generative models in the immense chemical space towards materials with target properties. Applied to the design of alloy electrocatalysts for CO2 reduction (CO2RR), MAGECS generates over 250,000 structures, achieving a 2.5-fold increase in high-activity structures (35%) compared to random generation. Five predicted alloys— CuAl, AlPd, Sn2Pd5, Sn9Pd7, and CuAlSe2 are synthesized and characterized, with two showing around 90% Faraday efficiency for CO2RR. This work highlights the potential of MAGECS to revolutionize functional material development, paving the way for fully automated, artificial intelligence-driven material design. Designing materials with optimal properties is a longstanding challenge, as current methods struggle to explore the vast chemical space effectively. Here, the authors combine generative model with optimization methods to design novel and highly active alloy electrocatalysts for CO2 electroreduction.