Publication date: 15th December 2025
High-throughput (HT) experimentation is increasingly shaping the way electrocatalysts are discovered and assessed, offering the ability to rapidly map composition–property relationships across vast chemical spaces. Yet, as in many areas of electrochemical energy research, practical limitations introduce an inherent tradeoff: fast activity screening enables broad sampling at scale, but often emphasizes throughput over detailed mechanistic insight. Conversely, deeper multimodal characterization can deliver deep mechanistic understanding but naturally limits experimental throughput and slows down knowledge acquisition.[1] Navigating this balance is especially important for modern, data-centric materials discovery, where robust structure–activity–stability correlations are essential for training reliable predictive machine-learning models.[2]
This talk will introduce an approach aimed at addressing the throughput–knowledge tradeoff by integrating Gaussian process regression (GPR) with comprehensive HT electrochemical and structural characterization. Using IrCoTi mixed-oxide thin-film libraries reactively sputtered at room temperature and 500 °C as a model platform, it will be demonstrated how synthesis conditions, crystallinity, and phase segregation can influence the acidic oxygen evolution reaction performance and durability. Special emphasis will be placed on operando dissolution analysis using a scanning flow cell coupled to inductively coupled plasma mass spectrometry (SFC-ICP-MS), which provides insight into how instability can shape observed activity trends and underscores the importance of including stability considerations for interpreting HT electrocatalyst activity screening outcomes.
Drawing on results from our recent work and related studies, the talk will highlight how coupling GPR with HT multimodal workflows can enhance data generation rates without sacrificing mechanistic depth.[3] This combined strategy provides a transferable blueprint for AI-guided catalyst discovery, offering a pathway toward the digitally accelerated development of energy-relevant materials and devices.
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