Peichen Zhong , Bowen Deng , Tanjin He , Zhengyan Lun , Gerbrand Ceder
{"title":"Deep learning of experimental electrochemistry for battery cathodes across diverse compositions","authors":"Peichen Zhong , Bowen Deng , Tanjin He , Zhengyan Lun , Gerbrand Ceder","doi":"10.1016/j.joule.2024.03.010","DOIUrl":null,"url":null,"abstract":"<div><p>Artificial intelligence (AI) has emerged as a tool for discovering and optimizing novel battery materials. However, the adoption of AI in battery cathode representation and discovery is still limited due to the complexity of optimizing multiple performance properties and the scarcity of high-fidelity data. We present a machine learning model (DRXNet) for battery informatics and demonstrate the application in the discovery and optimization of disordered rocksalt (DRX) cathode materials. We have compiled the electrochemistry data of DRX cathodes over the past 5 years, resulting in a dataset of more than 19,000 discharge voltage profiles on diverse chemistries spanning 14 different metal species. Learning from this extensive dataset, our DRXNet model can capture critical features in the cycling curves of DRX cathodes under various conditions. Our approach offers a data-driven solution to facilitate the rapid identification of novel cathode materials, accelerating the development of next-generation batteries for carbon neutralization.</p></div>","PeriodicalId":343,"journal":{"name":"Joule","volume":"8 6","pages":"Pages 1837-1854"},"PeriodicalIF":38.6000,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Joule","FirstCategoryId":"88","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2542435124001454","RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, PHYSICAL","Score":null,"Total":0}
引用次数: 0
Abstract
Artificial intelligence (AI) has emerged as a tool for discovering and optimizing novel battery materials. However, the adoption of AI in battery cathode representation and discovery is still limited due to the complexity of optimizing multiple performance properties and the scarcity of high-fidelity data. We present a machine learning model (DRXNet) for battery informatics and demonstrate the application in the discovery and optimization of disordered rocksalt (DRX) cathode materials. We have compiled the electrochemistry data of DRX cathodes over the past 5 years, resulting in a dataset of more than 19,000 discharge voltage profiles on diverse chemistries spanning 14 different metal species. Learning from this extensive dataset, our DRXNet model can capture critical features in the cycling curves of DRX cathodes under various conditions. Our approach offers a data-driven solution to facilitate the rapid identification of novel cathode materials, accelerating the development of next-generation batteries for carbon neutralization.
期刊介绍:
Joule is a sister journal to Cell that focuses on research, analysis, and ideas related to sustainable energy. It aims to address the global challenge of the need for more sustainable energy solutions. Joule is a forward-looking journal that bridges disciplines and scales of energy research. It connects researchers and analysts working on scientific, technical, economic, policy, and social challenges related to sustainable energy. The journal covers a wide range of energy research, from fundamental laboratory studies on energy conversion and storage to global-level analysis. Joule aims to highlight and amplify the implications, challenges, and opportunities of novel energy research for different groups in the field.