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http://hdl.handle.net/10397/106800
Title: | Recent advances in the data-driven development of emerging electrocatalysts | Authors: | Ding, K Yang, T Leung, MT Yang, K Cheng, H Zeng, M Li, B Yang, M |
Issue Date: | Dec-2023 | Source: | Current opinion in electrochemistry, Dec. 2023, v. 42, 101404 | Abstract: | Data-driven strategies have proven efficient for the design of high-performance electrocatalysts in the vast material search space. In this review, we present an overview of data-driven approaches to emerging electrocatalyst design: high-throughput experiments, high-throughput calculations, and machine learning. High-throughput experiments facilitate rapid synthesis and characterization of electrocatalysts, leading to efficient exploration of various materials. High-throughput calculations predict and screen materials' properties, allowing for the identification of promising electrocatalysts. The integration of machine learning further augments these high-throughput approaches through critical insight extracted from the large dataset, fast prediction of materials’ performance, and optimization of material discovery. Employing these data-driven strategies synergistically could accelerate the development of electrocatalysts. Such advancements could promote green energy technologies and substantially contribute to mitigating the grand challenges posed by global climate change. | Publisher: | Elsevier Ltd. | Journal: | Current opinion in electrochemistry | ISSN: | 2451-9103 | EISSN: | 2451-9111 | DOI: | 10.1016/j.coelec.2023.101404 |
Appears in Collections: | Journal/Magazine Article |
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