Machine learning quantification of grain boundary defects for high efficiency perovskite solar cells

IF 23.2 2区材料科学 Q1 MATERIALS SCIENCE, COMPOSITES

Advanced Composites and Hybrid Materials Pub Date : 2024-11-14 DOI:10.1007/s42114-024-01060-9

Xiaohui Li, Yongxiang Mai, Haogang Meng, Huan Bi, Chi Huey Ng, Siow Hwa Teo, Chunfeng Lan, Putao Zhang, Shengjun Li

引用次数: 0

查看原文本刊更多论文

机器学习量化高效率过氧化物太阳能电池的晶界缺陷

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 Composites and Hybrid Materials Multiple-

CiteScore

26.00

自引率

21.40%

发文量

185

期刊介绍： Advanced Composites and Hybrid Materials is a leading international journal that promotes interdisciplinary collaboration among materials scientists, engineers, chemists, biologists, and physicists working on composites, including nanocomposites. Our aim is to facilitate rapid scientific communication in this field. The journal publishes high-quality research on various aspects of composite materials, including materials design, surface and interface science/engineering, manufacturing, structure control, property design, device fabrication, and other applications. We also welcome simulation and modeling studies that are relevant to composites. Additionally, papers focusing on the relationship between fillers and the matrix are of particular interest. Our scope includes polymer, metal, and ceramic matrices, with a special emphasis on reviews and meta-analyses related to materials selection. We cover a wide range of topics, including transport properties, strategies for controlling interfaces and composition distribution, bottom-up assembly of nanocomposites, highly porous and high-density composites, electronic structure design, materials synergisms, and thermoelectric materials. Advanced Composites and Hybrid Materials follows a rigorous single-blind peer-review process to ensure the quality and integrity of the published work.