Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userExcitonic Effects in Absorption Spectra of Carbon Dioxide Reduction Photocatalysts
100
0
0.0
(
0
)
Added by
Tathagata Biswas Mr
Publication date
2021
fields
Physics
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
The formation and disassociation of excitons plays a crucial role in any photovoltaic or photocatalytic application. However, excitonic effects are seldom considered in materials discovery studies due to the monumental computational cost associated with the examination of these properties. Here, we study the excitonic properties of nearly 50 photocatalysts using state-of-the-art Bethe-Salpeter formalism. These $sim$ 50 materials were recently recognized as promising photocatalysts for CO$_2$ reduction through a data-driven screening of 68,860 materials. Here, we propose three screening criteria based on the optical properties of these materials, taking excitonic effects into account, to further down select 6 materials. Remarkably we find a strong correlation between the exciton binding energies obtained from the Bethe-Salpeter formalism and those obtained from the computationally much less-expensive Wannier-Mott model for these chemically diverse $sim$ 50 materials. This work presents a new paradigm towards the inclusion of excitonic effects in future materials discovery for solar-energy harvesting applications.
rate research
Read More
With the examples of the C $K$-edge in graphite and the B $K$-edge in hexagonal BN, we demonstrate the impact of vibrational coupling and lattice distortions on the X-ray absorption near-edge structure (XANES) in 2D layered materials. Theoretical XANES spectra are obtained by solving the Bethe-Salpeter equation of many-body perturbation theory, including excitonic effects through the correlated motion of core-hole and excited electron. We show that accounting for zero-point motion is important for the interpretation and understanding of the measured X-ray absorption fine structure in both materials, in particular for describing the $sigma^*$-peak structure.
The Li2SiO3 compound, a ternary electrolyte compound of Lithium-ion based batteries, exhibits unique geometric and band structures, an atom-dominated energy spectrum, charge densities distributions, atom and orbital-projected density of states, and strong optical responses. The state-of-the-art analysis, based on an ab-initio simulation, have successfully confirmed the concise physical/chemical picture and the orbital bonding in Li-O and Si-O bonds. Additionally, the unusual optical response behavior includes a large redshift of the onset frequency due to the extremely strong excitonic effect, the polarization of optical properties along three-directions, 22 optical excitations structures and the most prominent plasmon mode in terms of the dielectric functions, energy loss functions, absorption coefficients, and reflectance spectra. The close connections of electronic and optical properties can identify a specific orbital hybridization for each distinct excitation channel. The developed theoretical framework will be very appropriate for fully comprehending the diverse phenomena of cathode/electrolyte/anode materials in ion-based batteries.
We find that the optical properties of carbon nanotubes reflect remarkably strong effects of exciton-phonon coupling. Tight-binding calculations show that a significant fraction of the spectral weight of the absorption peak is transferred to a distinct exciton+phonon sideband, which is peaked at around 200 meV above the main absorption peak. This sideband provides a distinctive signature of the excitonic character of the optical transition. The exciton-phonon coupling is reflected in a dynamical structural distortion, which contributes a binding energy of up to 100 meV. The distortion is surprisingly long-ranged, and is strongly dependent on chirality.
The advent of massive data repositories has propelled machine learning techniques to the front lines of many scientific fields, and exploring new frontiers by leveraging the predictive power of machine learning will greatly accelerate big data-assisted discovery. In this work, we show that graph-based neural networks can be used to predict the near edge x-ray absorption structure spectra of molecules with exceptional accuracy. The predicted spectra reproduce nearly all the prominent peaks, with 90% of the predicted peak locations within 1 eV of the ground truth. Our study demonstrates that machine learning models can achieve practically the same accuracy as first-principles calculations in predicting complex physical quantities, such as spectral functions, but at a fraction of the cost.
We study the effect of quantum vibronic coupling on the electronic properties of carbon allotropes, including molecules and solids, by combining path integral first principles molecular dynamics (FPMD) with a colored noise thermostat. In addition to avoiding several approximations commonly adopted in calculations of electron-phonon coupling, our approach only adds a moderate computational cost to FPMD simulations and hence it is applicable to large supercells, such as those required to describe amorphous solids. We predict the effect of electron-phonon coupling on the fundamental gap of amorphous carbon, and we show that in diamond the zero-phonon renormalization of the band gap is larger than previously reported.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
