Autonomous Catalyst Explorer—Accelerating Data-Driven Optimization of Electrocatalysts for Electrochemical CO₂ Reduction

The development of energy-dense electro-fuels via electrochemical CO₂ reduction is pivotal for advancing sustainable energy solutions. We introduce the “Catalyst Explorer,” an autonomous, mobile, and adaptive platform that accelerates electrocatalyst discovery and optimization across diverse local conditions. By integrating automated catalyst preparation, electrochemical testing, and machine learning-driven closed-loop decision-making, the system effectively navigates complex parameter spaces. Employing a multi-objective Bayesian optimization framework, it optimizes critical variables such as copper-based electrodeposition parameters, CO₂ partial pressure, and ionic liquid composition, achieving enhanced Faradaic efficiency and current density. Built on open-source software and modular hardware, the Catalyst Explorer offers a scalable, cost-effective approach to high-throughput experimentation using existing laboratory instruments. This platform underscores the transformative potential of autonomous laboratories to drive rapid, data-driven innovations in electrocatalysis.