Evolutionary mapping across vast genetic space drives the discovery of causal gene blocks for designing high-potential aromatic cathodes

Abstract

Optimizing redox-active organic compounds is crucial for next-generation battery technologies, particularly because these compounds show promise as sustainable, high-performance cathode materials. Despite the potential of aromatic architectures to enhance electronic conductivity, the perception that aromatic backbones hinder redox properties has discouraged their use in cathode design. In this study, we introduce a genetic algorithm-assisted protocol for optimizing the redox potential of aromatic benzene-framed organic compounds. Leveraging a genetic algorithm and density functional theory calculations, we navigate a vast chemical space of 30 genetic components to identify promising compounds. The top-performing candidate has a redox potential of 3.11 V vs. Li/Li⁺, surpassing traditional non-aromatic 1,4-benzoquinone. The key to success is the identification of critical gene combinations, particularly involving boron and phosphorus as well as bent polar carbonyl groups, which significantly enhances electron affinity. This study provides a scalable framework for efficiently optimizing organic cathode materials through the iterative genetic reorganizations of building blocks. These findings pave the way for the accelerated development of advanced energy storage systems through computational material design.