Chrome Extension
WeChat Mini Program
Use on ChatGLM

Optical Gaps of Ionic Materials from GW/BSE-in-DFT and CC2-in-DFT.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION(2024)

Friedrich Schiller Univ Jena

Cited 0|Views1
Abstract
This work presents a density functional theory (DFT)-based embedding technique for the calculation of optical gaps in ionic solids. The approach partitions the supercell of the ionic solid and embeds a small molecule-like cluster in a periodic environment using a cluster-in-periodic embedding method. The environment is treated with DFT, and its influence on the cluster is captured by a DFT-based embedding potential. The optical gap is estimated as the lowest singlet excitation energy of the embedded cluster, obtained using a wave function theory method: second-order approximate coupled-cluster singles and doubles (CC2), and a many-body perturbation theory method: GW approximation combined with the Bethe-Salpeter equation (GW/BSE). The calculated excitation energies are benchmarked against the periodic GW/BSE values, equation-of-motion coupled-cluster singles and doubles (EOM-CCSD) results, and experiments. Both CC2-in-DFT and GW/BSE-in-DFT deliver excitation energies that are in good agreement with experimental values for several ionic solids (MgO, CaO, LiF, NaF, KF, and LiCl) while incurring negligible computational costs. Notably, GW/BSE-in-DFT exhibits remarkable accuracy with a mean absolute error (MAE) of just 0.38 eV with respect to experiments, demonstrating the effectiveness of the embedding strategy. In addition, the versatility of the method is highlighted by investigating the optical gap of a 2D MgCl2 system and the excitation energy of an oxygen vacancy in MgO, with results in good agreement with reported values.
More
Translated text
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Related Papers
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper

要点】:本研究提出了一种基于密度泛函理论(DFT)的嵌入技术,用于计算离子固体的光学带隙,通过将离子固体的超胞分区并在周期环境中嵌入小分子簇,结合CC2和GW/BSE方法,实现了计算效率与准确度的平衡。

方法】:采用DFT-based嵌入技术,将离子固体超胞分区后,使用簇-周期嵌入方法在周期环境中嵌入小分子簇,并分别通过CC2和GW/BSE方法计算嵌入簇的最低单重激发能量。

实验】:通过对比CC2-in-DFT和GW/BSE-in-DFT计算的光学带隙与周期GW/BSE值、EOM-CCSD结果以及实验值,在MgO、CaO、LiF、NaF、KF和LiCl等几种离子固体上验证了方法的准确性。实验结果显示,GW/BSE-in-DFT的平均绝对误差(MAE)仅为0.38 eV,且该方法还适用于二维MgCl2系统和MgO中氧空位的激发能量计算。