{"title":"","authors":"Connor D Amelung, and , Sharon Gerecht*, ","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":72040,"journal":{"name":"Accounts of materials research","volume":"6 5","pages":"XXX-XXX XXX-XXX"},"PeriodicalIF":14.0,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/pdf/10.1021/accountsmr.4c00390","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144447721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Man Li, Suixuan Li, Zhihan Zhang, Chuanjin Su, Bryce Wong and Yongjie Hu*,
{"title":"","authors":"Man Li, Suixuan Li, Zhihan Zhang, Chuanjin Su, Bryce Wong and Yongjie Hu*, ","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":72040,"journal":{"name":"Accounts of materials research","volume":"6 5","pages":"XXX-XXX XXX-XXX"},"PeriodicalIF":14.0,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/pdf/10.1021/accountsmr.4c00349","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144447726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Biphen[n]arene-Based Supramolecular Materials","authors":"Zhixue Liu, Junyi Chen, Chunju Li","doi":"10.1021/accountsmr.5c00071","DOIUrl":"https://doi.org/10.1021/accountsmr.5c00071","url":null,"abstract":"Macrocycles play pivotal roles in supramolecular chemistry and materials science because of their distinctive molecular recognition capabilities and versatile applications in self-assembly. However, traditional macrocycles, such as cyclodextrins, calixarenes, cucurbiturils, and pillararenes, have inherent limitations in terms of cavity size and structural variety, which restrict their ability to encapsulate guest molecules of varying sizes and their potential in constructing multifunctional materials. To address these challenges, our group has developed a simple, universal, and modular strategy for constructing functional macrocycles, termed biphen[<i>n</i>]arenes. This approach leverages structure- or function-oriented modular replacement of reactive, functional, and linking modules. Therefore, biphen[<i>n</i>]arenes with customized cavity size and molecule depth can effectively encapsulate guests from small molecules to biomacromolecules. On the other hand, different from modification of side chains, incorporation of functional primitives into the biphen[<i>n</i>]arene scaffold can leave active sites on both edges to induce additional moieties to improve recognition potency or integrate extra application functionality. These characteristics provide significant advantages in the construction of diverse supramolecular materials.","PeriodicalId":72040,"journal":{"name":"Accounts of materials research","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144114500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Machine Learning Assisted Material Discovery: A Small Data Approach","authors":"Qionghua Zhou, Xinyu Chen, Jinlan Wang","doi":"10.1021/accountsmr.1c00236","DOIUrl":"https://doi.org/10.1021/accountsmr.1c00236","url":null,"abstract":"The data-driven paradigm, represented by the famous machine learning paradigm, is revolutionizing the way materials are discovered. The inductive nature of the data-driven approach gives it great speed of prediction but also brings with it a heavy reliance on material data. However, unlike its success with text and images, which are supported by big data, materials data tend to be small data. Building a large database of materials is a good solution but not a permanent one. The cost of materials data is much higher than that of text or images, and the size of the materials database at this stage is far from sufficient. We will continue to face a shortage of materials data for a long time to come, making small data approaches necessary for machine learning based materials discovery.","PeriodicalId":72040,"journal":{"name":"Accounts of materials research","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144122766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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