Inverse Design of Promising Alloys for Electrocatalytic CO$_2$ Reduction via Generative Graph Neural Networks Combined with Bird Swarm Algorithm

TL;DR

This paper introduces MAGECS, a novel AI framework combining graph neural networks and bird swarm algorithms to efficiently explore vast chemical spaces for designing effective alloy electrocatalysts for CO₂ reduction, achieving high success rates.

Contribution

The study presents a new inverse design method that significantly improves exploration of chemical space for electrocatalysts using generative models and swarm optimization.

Findings

Generated over 250,000 promising alloy structures with high activity.

Proportion of desired structures increased 2.5-fold.

Successfully synthesized and experimentally validated five predicted alloys.

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 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.