Inverse Design of Promising Alloys for Electrocatalytic CO2 Reduction via Generative Graph Neural Networks Combined with Bird Swarm Algorithm
Abstract
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 CO2 reduction reaction (CO2RR) and works extremely well. Specifically, the chemical space of CO2RR 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, Sn2Pd5, Sn9Pd7, and CuAlSe2 are successfully synthesized and characterized experimentally, two of which exhibit about 90% Faraday efficiency of CO2RR, and CuAl achieved 76% efficiency for C2 products. This pioneering application of inverse design in CO2RR 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.
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