Benchtop Autonomous Electrochemical Characterization System for Combinatorial Thin-Film Solid Oxide Electrodes

The design of materials for electrochemical energy conversion is complicated by a vast search space of candidate materials and multifaceted property requirements: multicarrier conductivity, stability, and catalytic activity are all necessary but rarely intersect. Although self-driving laboratories are rapidly rising to address such material optimization problems, the required infrastructure for integrated, large-scale robotic facilities can be cost-prohibitive. Here we develop and evaluate a closed-loop measurement system for efficient screening of proton-conducting oxide electrodes for ceramic fuel cells and electrolyzers, building on top of an existing benchtop instrument and integrating techniques for rapid impedance measurement and automated analysis. This system exemplifies a “minimum viable” self-driving implementation that can deliver substantial benefits with relatively simple infrastructure. Combinatorial thin-film microelectrode libraries are characterized with a recently developed joint time-domain and 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 an active learning and Bayesian optimization process that learns to predict electrochemical impedance as a function of material composition, measurement temperature, oxygen partial pressure, and electrical bias, which further reduces the screening time by tenfold with optimized experimental sequences. We apply this system to <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" display="inline"> <a:mi>Ba</a:mi> <a:mo stretchy="false">(</a:mo> <a:mi>Co</a:mi> <a:mo>,</a:mo> <a:mi>Fe</a:mi> <a:mo>,</a:mo> <a:mi>Zr</a:mi> <a:mo>,</a:mo> <a:mrow> <a:mi mathvariant="normal">Y</a:mi> </a:mrow> <a:mo stretchy="false">)</a:mo> <a:msub> <a:mrow> <a:mi mathvariant="normal">O</a:mi> </a:mrow> <a:mrow> <a:mn>3</a:mn> <a:mo>−</a:mo> <a:mi>δ</a:mi> </a:mrow> </a:msub> </a:math> combinatorial libraries and evaluate its effectiveness for learning material property trends and optimizing expensive-to-evaluate properties such as activation energy. This offers insights into key methodological aspects of practical autonomous experimentation, including surrogate model validation, cost-aware acquisition functions, and high-throughput data interpretation. Our results demonstrate the efficacy of the system for rapidly gathering information, but also highlight real-world experimental challenges of thin-film degradation and numerical instability in surrogate models.