Symposium EN05: Electrodes for Chemical and Energy Conversion Technologies

Joseph H. Montoya, Toyota Research Institute 

Practical Materials AI for Improving Electrochemical Stability 
Written by In Young Park 

As we transition to cleaner energy, the need for electrochemically stable and durable materials is greater than ever, requiring a deeper integration of theory and practice. Pourbaix diagrams, essential for understanding phase stability and electrochemical properties, now incorporate multi-element systems using voltage as a convex hull, expanding analysis from three to eight or more components. The SCAN functional has significantly enhanced density functional theory (DFT) accuracy without requiring empirical corrections, surpassing traditional methods like Perdew-Burke-Ernzerhof (PBE). In battery research, tools like Toyota Research Institute’s novel platform automate cycling experiments and use machine learning to predict performance degradation, optimizing durability. For catalysts, high-throughput Inductively Coupled Plasma Mass Spectrometry (ICP-MS) and automated systems reveal oxygen reduction reaction (ORR) mechanisms, while nanoprinted particle libraries map electrochemical activity on a large scale. These advancements bridge theoretical understanding with practical testing, addressing critical challenges in battery and fuel cell technologies as we strive for a more sustainable energy future.