Artificial Intelligence-Assisted Experimental Optimization of Water Oxidation Catalysts

被引：0

作者：

Spitzenpfeil, Henrik ^{[1
]}

Neumann, Marius ^{[1
]}

Hausen, Nick ^{[1
]}

Palkovits, Regina ^{[1
,2
]}

Palkovits, Stefan ^{[1
]}

机构：

[1] Rhein Westfal TH Aachen, Heterogeneous Catalysis & Chem Technol, Worringerweg 2, D-52074 Aachen, Germany

[2] Forschungszentrum Julich GmbH, Inst nachhaltige Wasserstoffwirtschaft 2, Marie Curie Str 5, D-52428 Julich, Germany

来源：

CHEMIE INGENIEUR TECHNIK | 2025年 / 97卷 / 05期

关键词：

Active learning; Catalysis; Electrochemistry; Machine learning; Oxygen evolution reaction; HIGH-THROUGHPUT EXPERIMENTATION;

D O I：

10.1002/cite.202400156

中图分类号：

TQ [化学工业];

学科分类号：

0817 ;

摘要：

Artificial intelligence (AI) methods are very often used to make predictions for datasets that were created externally in arbitrary experiments or on already literature known datasets. In this work, we try to make use of active learning techniques to search for an optimal strategy for the startup-phase of bulk nickel electrodes in the oxygen evolution reaction. The data collected was afterwards reduced in dimensions and used to extract additional information that were learned via an artificial neural network (ANN) on the dataset, respectively.

引用

页码：472 / 478

页数：7

共 27 条

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