Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/106800
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Applied Physics | - |
dc.contributor | Department of Computing | - |
dc.creator | Ding, K | - |
dc.creator | Yang, T | - |
dc.creator | Leung, MT | - |
dc.creator | Yang, K | - |
dc.creator | Cheng, H | - |
dc.creator | Zeng, M | - |
dc.creator | Li, B | - |
dc.creator | Yang, M | - |
dc.date.accessioned | 2024-06-04T07:39:50Z | - |
dc.date.available | 2024-06-04T07:39:50Z | - |
dc.identifier.issn | 2451-9103 | - |
dc.identifier.uri | http://hdl.handle.net/10397/106800 | - |
dc.language.iso | en | en_US |
dc.publisher | Elsevier Ltd. | en_US |
dc.title | Recent advances in the data-driven development of emerging electrocatalysts | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 42 | - |
dc.identifier.doi | 10.1016/j.coelec.2023.101404 | - |
dcterms.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. | - |
dcterms.accessRights | embargoed access | en_US |
dcterms.bibliographicCitation | Current opinion in electrochemistry, Dec. 2023, v. 42, 101404 | - |
dcterms.isPartOf | Current opinion in electrochemistry | - |
dcterms.issued | 2023-12 | - |
dc.identifier.scopus | 2-s2.0-85175312050 | - |
dc.identifier.eissn | 2451-9111 | - |
dc.identifier.artn | 101404 | - |
dc.description.validate | 202406 bcch | - |
dc.identifier.FolderNumber | a2746 | en_US |
dc.identifier.SubFormID | 48201 | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | The Hong Kong Polytechnic University | en_US |
dc.description.pubStatus | Published | en_US |
dc.date.embargo | 2025-12-31 | en_US |
dc.description.oaCategory | Green (AAM) | en_US |
Appears in Collections: | Journal/Magazine Article |
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