{"title":"外封面：第4卷第3期","authors":"","doi":"10.1002/idm2.12187","DOIUrl":null,"url":null,"abstract":"<p><b>Outside Front Cover</b>: The article of doi:10.1002/idm2.12249 explores how machine learning–driven activity prediction, energy barrier optimization, and data-guided materials design accelerate the discovery of a new generation of electrocatalysts, and discusses their applications in water electrolysis, fuel cells, and carbon dioxide reduction, thereby advancing innovation in sustainable energy solutions.\n\n <figure>\n <div><picture>\n <source></source></picture><p></p>\n </div>\n </figure></p>","PeriodicalId":100685,"journal":{"name":"Interdisciplinary Materials","volume":"4 3","pages":""},"PeriodicalIF":24.5000,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/idm2.12187","citationCount":"0","resultStr":"{\"title\":\"Outside Front Cover: Volume 4 Issue 3\",\"authors\":\"\",\"doi\":\"10.1002/idm2.12187\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><b>Outside Front Cover</b>: The article of doi:10.1002/idm2.12249 explores how machine learning–driven activity prediction, energy barrier optimization, and data-guided materials design accelerate the discovery of a new generation of electrocatalysts, and discusses their applications in water electrolysis, fuel cells, and carbon dioxide reduction, thereby advancing innovation in sustainable energy solutions.\\n\\n <figure>\\n <div><picture>\\n <source></source></picture><p></p>\\n </div>\\n </figure></p>\",\"PeriodicalId\":100685,\"journal\":{\"name\":\"Interdisciplinary Materials\",\"volume\":\"4 3\",\"pages\":\"\"},\"PeriodicalIF\":24.5000,\"publicationDate\":\"2025-05-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/idm2.12187\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Interdisciplinary Materials\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/idm2.12187\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CHEMISTRY, PHYSICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Interdisciplinary Materials","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/idm2.12187","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, PHYSICAL","Score":null,"Total":0}
Outside Front Cover: The article of doi:10.1002/idm2.12249 explores how machine learning–driven activity prediction, energy barrier optimization, and data-guided materials design accelerate the discovery of a new generation of electrocatalysts, and discusses their applications in water electrolysis, fuel cells, and carbon dioxide reduction, thereby advancing innovation in sustainable energy solutions.
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