Perumal, Sakthivel Han, Da Bean Marimuthu, Thandapani Lim, Taewaen Kim, Hyun Woo Seo, Junhyeok 2025-04-09T09:00:40Z 2025-04-09T09:00:40Z 2025-04-09 2025-07 1616-301X https://pubs.kist.re.kr/handle/201004/152243 High-entropy nanoparticles (HENPs) present a vast opportunity for the development of advanced electrocatalysts. The optimization of their chemical compositions, including the careful selection and combination of elements, is critical to tailoring HENPs for specific catalytic processes. To reduce the extensive experimental effort involved in composition optimization, active learning techniques can be utilized to predict and suggest materials with enhanced electrocatalytic activity. In this study, sub-2 nm high-entropy catalysts incorporating eight transition metal elements are developed through an active learning workflow aimed at identifying optimal compositions. Using initial experimental data, the approach successfully guided the discovery of a new octonary HENP catalyst with state-of-the-art performance in the hydrogen evolution reaction (HER). Catalyst performance is improved within the prediction uncertainty of the machine learning model. For the oxygen evolution reaction (OER), however, the initial model demonstrated limited predictive accuracy, leading to an assessment of the workflow's boundaries. These findings underscore how the integration of curated experimental data with active learning can accelerate electrocatalyst discovery, while also highlighting critical areas for further model refinement. English John Wiley & Sons Ltd. Active Learning-Driven Discovery of Sub-2 Nm High-Entropy Nanocatalysts for Alkaline Water Splitting Article 10.1002/adfm.202424887 1 Advanced Functional Materials, v.35, no.30 Advanced Functional Materials 35 30 Y scie scopus 001445618500001 2-s2.0-105000248310 Chemistry, Multidisciplinary Chemistry, Physical Nanoscience & Nanotechnology Materials Science, Multidisciplinary Physics, Applied Physics, Condensed Matter Chemistry Science & Technology - Other Topics Materials Science Physics Article OXIDES NANOPARTICLES ALLOYS DESIGN active learning Bayesian optimization hydrogen evolution reaction oxygen evolution reaction high entropy nanoparticles