Rapid discovery of performant electrocatalysts is vital for a carbon-neutral economy. In drug development and structural biology, acceleration in discovery¹ has been driven by machine-learning (ML) models trained on vast, standardised experimental datasets that map structure to function.² Although computational screening of electrocatalysts and other electrochemical technologies has advanced similarly,³ there is still no experimental database of comparable scale or quality to validate predictions and power next-generation generative models. The obstacle is the disordered, nanometre-scale nature of electrocatalyst interfaces and the fact that many interfaces form during operation, meaning physical parameters which are predictive of performance must be measured in-situ. In oxygen evolution reaction (OER) performance descriptors are usually extracted only from idealised single-crystal systems that do not represent deployable materials.⁴ Of particular interest are binding energies – a descriptor for OER catalyst performance and the strength of adsorbate interactions which produce a coverage dependent perturbation to these values. Previously, we have shown that these values can be extracted by accurate measurements of minute changes in optical absorption that arise as a result of the generation of surface bound OER intermediates.^5–8 In this talk, I will present HiSpEC—the first high-throughput operando optical spectroelectrochemistry platform capable of extracting binding energy and interaction strengths at scale. HiSpEC pairs modular, hierarchical experiment orchestration with a physics-constrained, unsupervised ML workflow, enabling stable, automated measurements, with a throughput of roughly 100 compositions per day. The scale of the data, coupled to improved sensitivity and robust automated analysis reveals detailed trends in materials properties that were not previously observable, such as changes in binding energy and interactions driven by surface disorder. This dataset closes the experimental gap, allows rigorous testing of computational theories, and lays the foundation for generative ML models that can design and optimise next-generation energy materials at unprecedented speed.

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(2) Jumper, J., et. al. Nature 2021, 596 (7873), 583–589.

(3) Horton, M. K., et. al Nat. Mater. 2025, 1–11.

(4) Kuo, D.Y., et. al. J. Am. Chem. Soc. 2018, 140 (50), 17597–17605.

(5) Moss, B.; et. al J. Am. Chem. Soc. 2024, 146 (13), 8915–8927.

(6) Liang, C., et. al., J. Am. Chem. Soc. 2024, 146 (13), 8928–8938.

(7) Liang, C.; et. al., Nat. Catal. 2024, 7 (7), 763–775.

(8) Moss, B., et. al. Nat. Rev. Met. Primers (In press)