Autonomous electrochemical characterization system for combinatorial thin-film solid oxide electrodes

The design of materials for electrochemical energy conversion is complicated by multifaceted property requirements: multi-carrier conductivity, stability, and catalytic activity are all necessary but rarely intersect. Meanwhile, the large search space of candidate materials, the influence of measurement conditions like temperature and atmosphere, and time-intensive electrochemical characterization further slow down materials discovery efforts. Here, we develop and evaluate a system, including hardware and software, for efficient screening of proton-conducting oxide electrodes for ceramic fuel cells and electrolyzers. Combinatorial thin-film microelectrode libraries are characterized with a joint time-/frequency-domain impedance measurement technique, which provides an order-of-magnitude acceleration relative to conventional impedance spectroscopy. The distribution of relaxation times is extracted from impedance data and analyzed without human intervention. These results feed a Bayesian active learning process that learns to predict electrochemical impedance as a function of material composition, measurement temperature, oxygen partial pressure, and electrical bias, which further reduces screening time by 10-fold with optimized experimental sequences. We apply this system to Ba(Co,Fe,Zr,Y)O3-δ combinatorial libraries and evaluate its effectiveness for learning materials property trends and optimizing expensive-to-evaluate properties like activation energy. Our results demonstrate the efficacy of the system for rapidly gathering information, but also highlight potential challenges of thin-film degradation and numerical instability in surrogate models.