Gaussian process regression-enhanced screening of Ir–Co–Ti mixed-oxide thin-film electrocatalyst libraries for acidic oxygen evolution

Przybysz JM, Thelen F, Lourens F, Jenewein K, Ludwig A, Cherevko S (2026)

Publication Type: Journal article

Publication year: 2026

Journal

Digital Discovery Royal Society of Chemistry

DOI: 10.1039/d6dd00127k

Abstract

High-throughput (HT) experimentation is a powerful approach in electrocatalysis, enabling rapid exploration of composition–property relationships. However, HT studies often face a tradeoff between throughput and information depth: rapid activity screening offers speed but limited mechanistic understanding, whereas multimodal characterization yields richer insight at the expense of efficiency. The latter approach, most suited for fundamental research, yields highly valuable datasets for AI-driven scientific discovery. Here, we address the throughput-knowledge tradeoff by integrating Gaussian process regression with comprehensive HT characterization to increase the rate of data generation while preserving mechanistic detail. To illustrate this strategy, Ir–Co–Ti mixed oxide thin-film libraries reactively co-sputtered at room temperature and 500 °C are used as a model system to examine how synthesis conditions and phase constitution influence oxygen evolution reaction (OER) activity and stability under acidic conditions. Systematic characterization reveals that crystallinity and phase segregation govern performance, and that dissolution behavior, probed with operando inductively coupled plasma mass spectrometry downstream of a scanning flow cell (SFC-ICP-MS), provides essential context for interpreting activity trends. More broadly, the presented workflow offers a transferable strategy for integrating machine learning with multimodal HT methods to support data-driven catalyst discovery and development.

Involved external institutions

Forschungszentrum Jülich / Research Centre Jülich (FZJ) DE Germany (DE) Ruhr-Universität Bochum (RUB) DE Germany (DE)

How to cite

APA:

Przybysz, J.M., Thelen, F., Lourens, F., Jenewein, K., Ludwig, A., & Cherevko, S. (2026). Gaussian process regression-enhanced screening of Ir–Co–Ti mixed-oxide thin-film electrocatalyst libraries for acidic oxygen evolution. Digital Discovery. https://doi.org/10.1039/d6dd00127k

MLA:

Przybysz, Joanna M., et al. "Gaussian process regression-enhanced screening of Ir–Co–Ti mixed-oxide thin-film electrocatalyst libraries for acidic oxygen evolution." Digital Discovery (2026).

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