推进复杂微波材料的第一原理介电性能预测:元素单位分解方法

IF 9.4 1区 材料科学 Q1 CHEMISTRY, PHYSICAL npj Computational Materials Pub Date : 2024-08-13 DOI:10.1038/s41524-024-01366-8
Yabei Wu, Peihong Zhang, Wenqing Zhang
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引用次数: 0

摘要

钨青铜型材料 Ba6-3xRE8+2xTi18O54(RE = 稀土元素)是一种重要的微波介质,因其介电常数高、损耗低、可调谐性强,在未来微波器件微型化方面大有可为,但仍有很大的改进空间。第一原理计算具有公认的预测能力,可以减少或消除昂贵而耗时的实验试错过程,从而大大有助于加速材料优化。然而,微波介质(如钨青铜类材料)是相当复杂的系统,其单元格包含数百或数千个原子,这使得反初始计算的成本过高。在这项工作中,我们提出了一种元素单元分解(EUD)技术,它可以大大减少预测复杂微波介质性质的计算工作量,并证明了其准确性和效率。我们的方法为复杂微波介电材料的第一原理预测和设计提供了便利,否则这将是极其困难的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Advancing first-principles dielectric property prediction of complex microwave materials: an elemental-unit decomposition approach

Tungsten-bronze-type material Ba6-3xRE8+2xTi18O54, (RE = rare earth elements) is an important microwave dielectric that has shown great promises for future miniaturization of microwave devices because of its high dielectric constant, low loss, and tunabilities, and there is still much room for improvement. With their proven predictive power, first-principles calculations may greatly help accelerate materials optimization by reducing or eliminating the expensive and time-consuming experimental trial-and-error process. However, microwave dielectrics such as the tungsten-bronze-type materials are rather complex systems with unit cells containing hundreds or thousands of atoms, making ab initio calculations prohibitively expensive. In this work, we propose an elemental-unit decomposition (EUD) technique that can drastically reduce the computational effort of predicting the properties of complex microwave dielectrics and demonstrate its accuracy and efficiency. Our approach facilitates first-principles prediction and design of complex microwave dielectric materials that would otherwise be extremely difficult.

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来源期刊
npj Computational Materials
npj Computational Materials Mathematics-Modeling and Simulation
CiteScore
15.30
自引率
5.20%
发文量
229
审稿时长
6 weeks
期刊介绍: npj Computational Materials is a high-quality open access journal from Nature Research that publishes research papers applying computational approaches for the design of new materials and enhancing our understanding of existing ones. The journal also welcomes papers on new computational techniques and the refinement of current approaches that support these aims, as well as experimental papers that complement computational findings. Some key features of npj Computational Materials include a 2-year impact factor of 12.241 (2021), article downloads of 1,138,590 (2021), and a fast turnaround time of 11 days from submission to the first editorial decision. The journal is indexed in various databases and services, including Chemical Abstracts Service (ACS), Astrophysics Data System (ADS), Current Contents/Physical, Chemical and Earth Sciences, Journal Citation Reports/Science Edition, SCOPUS, EI Compendex, INSPEC, Google Scholar, SCImago, DOAJ, CNKI, and Science Citation Index Expanded (SCIE), among others.
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