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Excitonic Hamiltonians for Calculating Optical Absorption Spectra and Optoelectronic Properties of Molecular Aggregates and Solids
Published on: May 27, 2020
A Combined Wave Function and Density Functional Approach for K-Edge X-ray Absorption Near-Edge Spectroscopy: A Case
Soumen Ghosh1, Shaul Mukamel2, Niranjan Govind1
1Physical and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, United States.
Multiconfiguration pair-density functional theory (MC-PDFT) accurately predicts X-ray absorption spectra (XAS) for transition metals. This method offers a computationally efficient alternative to RASPT2 for core-level spectroscopy.
Area of Science:
- Computational Chemistry
- Spectroscopy
- Quantum Chemistry
Background:
- X-ray absorption spectra (XAS) prediction is crucial for chemistry and biology.
- Transition metal ions are key components in many chemical and biological systems.
Purpose of the Study:
- To evaluate the predictive accuracy of multiconfiguration pair-density functional theory (MC-PDFT) for XAS.
- To compare MC-PDFT with experimental data and other computational methods like RASPT2 and TDDFT.
- To assess the performance for metal K pre-edge features of aquated 3d transition metal ions.
Main Methods:
- Calculated metal K pre-edge features using MC-PDFT.
- Compared MC-PDFT results with experimental XAS data.
- Benchmarked MC-PDFT against restricted active-space second-order perturbation theory (RASPT2) and time-dependent density functional theory (TDDFT).
Main Results:
- TDDFT accurately predicts spectra dominated by single excitations but struggles with higher-order excitations.
- RASPT2 and MC-PDFT provide quantitatively accurate results for all excited states.
- MC-PDFT achieves accuracy comparable to RASPT2 but with significantly lower computational cost.
Conclusions:
- MC-PDFT is a reliable and computationally efficient method for predicting XAS of transition metal complexes.
- MC-PDFT presents a promising alternative for core-level spectroscopy, overcoming the high computational expense of RASPT2.
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