非经验万尼尔定位优化调整筛选范围分离混合函数在层状材料中的激发

IF 9.4 1区 材料科学 Q1 CHEMISTRY, PHYSICAL
María Camarasa-Gómez, Stephen E. Gant, Guy Ohad, Jeffrey B. Neaton, Ashwin Ramasubramaniam, Leeor Kronik
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Excitations in layered materials from a non-empirical Wannier-localized optimally- tuned screened range-separated hybrid functional

Excitations in layered materials from a non-empirical Wannier-localized optimally- tuned screened range-separated hybrid functional

Accurate prediction of electronic and optical excitations in van der Waals (vdW) materials is a long-standing challenge for density functional theory. The recent Wannier-localized optimally-tuned screened range-separated hybrid (WOT-SRSH) functional has proven successful in non-empirical determination of electronic band gaps and optical absorption spectra for covalent and ionic crystals. However, for vdW materials the tuning of the material- and structure-dependent functional parameters has only been attained semi-empirically. Here, we present a non-empirical WOT-SRSH approach applicable to vdW materials, with the optimal functional parameters transferable between monolayer and bulk. We apply this methodology to prototypical vdW materials: black phosphorus, molybdenum disulfide, and hexagonal boron nitride (in the latter case including zero-point renormalization). We show that the WOT-SRSH approach consistently achieves accuracy levels comparable to experiments and many-body perturbation theory (MBPT) calculations for band structures and optical absorption spectra, both on its own and as an optimal starting point for MBPT calculations.

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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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