  Directly generating material structures with optimal properties is a
long-standing goal in material design. One of the fundamental challenges lies
in how to overcome the limitation of traditional generative models to
efficiently explore the global chemical space rather than a small localized
space. Herein, we develop a framework named MAGECS to address this dilemma, by
integrating the bird swarm algorithm and supervised graph neural network to
effectively navigate the generative model in the immense chemical space towards
materials with target properties. As a demonstration, MAGECS is applied to
design compelling alloy electrocatalysts for CO$_2$ reduction reaction
(CO$_2$RR) and works extremely well. Specifically, the chemical space of
CO$_2$RR is effectively explored, where over 250,000 promising structures with
high activity have been generated and notably, the proportion of desired
structures is 2.5-fold increased. Moreover, five predicted alloys, i.e., CuAl,
AlPd, Sn$_2$Pd$_5$, Sn$_9$Pd$_7$, and CuAlSe$_2$ are successfully synthesized
and characterized experimentally, two of which exhibit about 90% Faraday
efficiency of CO$_2$RR, and CuAl achieved 76% efficiency for C$_2$ products.
This pioneering application of inverse design in CO$_2$RR catalysis showcases
the potential of MAGECS to dramatically accelerate the development of
functional materials, paving the way for fully automated, artificial
intelligence-driven material design.
