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…

• NN
  National Natural Science Foundation of China

  Awards: 22033002, 92261112, T2321002, 2021YFA1500700, 22373013
• GO
  Government of Jiangsu Province
• SU
  Southeast University
• BR
  Basic Research Program of Jiangsu Province

  Awards: BK20222007, BK20232012
• NK
  National Key Research and Development Program of China

  Awards: 2022YFA1503103, 2021YFA1500700
• NS
  National Supercomputing Center of Tianjin
• NS
  National Supercomputing Center, Korea Institute of Science and Technology Information