Quantitative prediction of optical static refractive index in complex oxides

IF 9.4 1区 材料科学 Q1 CHEMISTRY, PHYSICAL
Lan Yang, Xiao Zhou, Xudong Ni, Li Huang, Lianduan Zeng, Zhongyang Wang, Jun Song, Tongxiang Fan
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Abstract

The optical static refractive index, a critical intrinsic property of materials, plays a vital role in advanced optoelectronic applications. Accurate prediction of this index is essential for the efficient design and optimization of materials with tailored optical properties. Here, we present a robust predictive model that accurately forecasts the optical static refractive indices of complex oxides across diverse crystal structures and compositions. By leveraging chemical bond theory, our model elucidates the influence of intrinsic physical properties, including chemical bonds and d-electron bands, on the refractive index. Through rigorous analysis of 41 complex oxide systems and 5 doped systems, we demonstrate that our predictions align closely with experimental data, showcasing the model’s high accuracy and broad applicability. This work not only accelerates the development of novel materials and spectral design but also provides profound physical insights for optimizing and customizing optical properties.

Abstract Image

复合氧化物中光学静态折射率的定量预测
光学静态折射率是材料的一种重要的固有特性,在先进的光电应用中起着至关重要的作用。该指数的准确预测对于具有定制光学特性的材料的有效设计和优化至关重要。在这里,我们提出了一个强大的预测模型,可以准确地预测不同晶体结构和成分的复杂氧化物的光学静态折射率。通过利用化学键理论,我们的模型阐明了包括化学键和d电子带在内的内在物理性质对折射率的影响。通过对41个复杂氧化物体系和5个掺杂体系的严格分析,我们证明了我们的预测与实验数据密切相关,展示了模型的高精度和广泛的适用性。这项工作不仅加速了新材料和光谱设计的发展,而且为优化和定制光学特性提供了深刻的物理见解。
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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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