Artificial Intelligence-Driven Design and Optimization of Bio-electrocatalytic CO2 Conversion Pathways

Unlocking the potential of CO 2 as a renewable carbon resource is paramount to achieving economy-wide decarbonization. Emerging bio-electrocatalytic CO₂ conversion (BECC) platforms, such as microbial electrosynthesis (MES) and related Biohybrid (electro-biological) systems, offer exceptional precision for synthesizing multi-carbon products. Yet, after many years of research, their progress remains hindered by the slow pace and expensive nature of wet-lab research. Building on recent advances in artificial intelligence (AI) and machine learning (ML), this perspective highlights a new data-driven paradigm in BECC innovation. We begin by outlining recent advances and persistent challenges of MES and Biohybrid systems. We then provide an overview of AI/ML workflows that show potential to accelerate BECC research with an emphasis on biocatalyst engineering, electrode and catalyst design, and reactor optimization. Through this wide-ranging analysis, we aim to catalyze the integration of AI/ML tools into BECC studies, supporting a new frontier in CO 2 conversion research and development.