Ultrahigh Specific Strength by Bayesian Optimization of Carbon Nanolattices (Adv. Mater. 14/2025)

IF 27.4 1区材料科学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Advanced Materials Pub Date : 2025-04-10 DOI:10.1002/adma.202570108

Peter Serles, Jinwook Yeo, Michel Haché, Pedro Guerra Demingos, Jonathan Kong, Pascal Kiefer, Somayajulu Dhulipala, Boran Kumral, Katherine Jia, Shuo Yang, Tianjie Feng, Charles Jia, Pulickel M. Ajayan, Carlos M. Portela, Martin Wegener, Jane Howe, Chandra Veer Singh, Yu Zou, Seunghwa Ryu, Tobin Filleter

{"title":"Ultrahigh Specific Strength by Bayesian Optimization of Carbon Nanolattices (Adv. Mater. 14/2025)","authors":"Peter Serles, Jinwook Yeo, Michel Haché, Pedro Guerra Demingos, Jonathan Kong, Pascal Kiefer, Somayajulu Dhulipala, Boran Kumral, Katherine Jia, Shuo Yang, Tianjie Feng, Charles Jia, Pulickel M. Ajayan, Carlos M. Portela, Martin Wegener, Jane Howe, Chandra Veer Singh, Yu Zou, Seunghwa Ryu, Tobin Filleter","doi":"10.1002/adma.202570108","DOIUrl":null,"url":null,"abstract":"<p><b>Bayesian Optimization of Carbon Nanolattices</b></p><p>Machine Learning designs new nanolattice geometries with the strength of carbon steel, but the density of Styrofoam, offering record strength-to-weight of lightweight materials. By implementing multi-objective Bayesian optimization in combination with two-photon polymerization and pyrolysis, these ultrahigh specific strength carbon nanolattices more than double the performance of benchmark materials. More details can be found in article number 2410651 by Peter Serles, Tobin Filleter, Seunghwa Ryu, and co-workers.\n\n <figure>\n <div><picture>\n <source></source></picture><p></p>\n </div>\n </figure></p>","PeriodicalId":114,"journal":{"name":"Advanced Materials","volume":"37 14","pages":""},"PeriodicalIF":27.4000,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/adma.202570108","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advanced Materials","FirstCategoryId":"88","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/adma.202570108","RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}

引用次数: 0

Abstract

Bayesian Optimization of Carbon Nanolattices

Machine Learning designs new nanolattice geometries with the strength of carbon steel, but the density of Styrofoam, offering record strength-to-weight of lightweight materials. By implementing multi-objective Bayesian optimization in combination with two-photon polymerization and pyrolysis, these ultrahigh specific strength carbon nanolattices more than double the performance of benchmark materials. More details can be found in article number 2410651 by Peter Serles, Tobin Filleter, Seunghwa Ryu, and co-workers.

Abstract Image

查看原文本刊更多论文

碳纳米晶格的超高比强度贝叶斯优化（Adv. Mater. 14/2025）

碳纳米晶格的贝叶斯优化机器学习设计了新的纳米晶格几何形状，具有碳钢的强度，但聚苯乙烯泡沫塑料的密度，提供轻质材料的创纪录强度重量比。通过实施多目标贝叶斯优化，结合双光子聚合和热解，这些超高比强度的碳纳米晶格的性能是基准材料的两倍以上。更多细节可以在Peter Serles， Tobin filletter， Seunghwa Ryu及其同事的文章编号2410651中找到。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Advanced Materials 工程技术-材料科学：综合

CiteScore

43.00

自引率

4.10%

发文量

2182

审稿时长

2 months

期刊介绍： Advanced Materials, one of the world's most prestigious journals and the foundation of the Advanced portfolio, is the home of choice for best-in-class materials science for more than 30 years. Following this fast-growing and interdisciplinary field, we are considering and publishing the most important discoveries on any and all materials from materials scientists, chemists, physicists, engineers as well as health and life scientists and bringing you the latest results and trends in modern materials-related research every week.