{"title":"Machine learning-guided design, synthesis, and characterization of atomically dispersed electrocatalysts","authors":"","doi":"10.1016/j.coelec.2024.101578","DOIUrl":null,"url":null,"abstract":"<div><p>The recent integration of machine learning into materials design has revolutionized the understanding of structure–property relationships and optimization of material properties beyond the trial-and-error paradigm. On one hand, machine learning has significantly accelerated the development of atomically dispersed metal-nitrogen-carbon (M-N-C) electrocatalysts, which traditionally heavily relied on heuristic approaches. On the other hand, the primary challenge of leveraging machine learning to expedite M-N-C materials discovery lies in the cost associated with data collection. We review recent machine learning integration strategies for M-N-C catalyst development, including discussions on the typical algorithms such as symbolic regression and convolutional neural networks employed for the theoretical design, synthesis optimization via active learning, and advanced microscopy characterization. Subsequently, we provide our perspective on potential near-future directions for furthering machine learning-assisted development of new M-N-C catalysts and elucidating the complex physicochemical mechanisms governing the selectivity, activity, and durability in this class of materials.</p></div>","PeriodicalId":11028,"journal":{"name":"Current Opinion in Electrochemistry","volume":null,"pages":null},"PeriodicalIF":7.9000,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current Opinion in Electrochemistry","FirstCategoryId":"92","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S245191032400139X","RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, PHYSICAL","Score":null,"Total":0}
引用次数: 0
Abstract
The recent integration of machine learning into materials design has revolutionized the understanding of structure–property relationships and optimization of material properties beyond the trial-and-error paradigm. On one hand, machine learning has significantly accelerated the development of atomically dispersed metal-nitrogen-carbon (M-N-C) electrocatalysts, which traditionally heavily relied on heuristic approaches. On the other hand, the primary challenge of leveraging machine learning to expedite M-N-C materials discovery lies in the cost associated with data collection. We review recent machine learning integration strategies for M-N-C catalyst development, including discussions on the typical algorithms such as symbolic regression and convolutional neural networks employed for the theoretical design, synthesis optimization via active learning, and advanced microscopy characterization. Subsequently, we provide our perspective on potential near-future directions for furthering machine learning-assisted development of new M-N-C catalysts and elucidating the complex physicochemical mechanisms governing the selectivity, activity, and durability in this class of materials.
期刊介绍:
The development of the Current Opinion journals stemmed from the acknowledgment of the growing challenge for specialists to stay abreast of the expanding volume of information within their field. In Current Opinion in Electrochemistry, they help the reader by providing in a systematic manner:
1.The views of experts on current advances in electrochemistry in a clear and readable form.
2.Evaluations of the most interesting papers, annotated by experts, from the great wealth of original publications.
In the realm of electrochemistry, the subject is divided into 12 themed sections, with each section undergoing an annual review cycle:
• Bioelectrochemistry • Electrocatalysis • Electrochemical Materials and Engineering • Energy Storage: Batteries and Supercapacitors • Energy Transformation • Environmental Electrochemistry • Fundamental & Theoretical Electrochemistry • Innovative Methods in Electrochemistry • Organic & Molecular Electrochemistry • Physical & Nano-Electrochemistry • Sensors & Bio-sensors •