相关论文: Deep Neural Network for Phonon-Assisted Optical Sp…
Using ab initio molecular-dynamics simulations combined with linear-response theory, we studied the x-ray absorption near-edge spectra (XANES) of a two-temperature dense copper plasma. As the temperature increases, XANES spectra exhibit a…
This work is directed to uncertainty quantification of homogenized effective properties for composite materials with complex, three dimensional microstructure. The uncertainties arise in the material parameters of the single constituents as…
An improved method of remote optical absorption spectroscopy and hyperspectral optical absorption imaging is described which takes advantage of the photoacoustic remote sensing detection architecture. A wide range of photoacoustic…
Confined polar optical phonons are studied in a semiconductor double heterostructure (SDH) by means of a generalization of a theory developed some years ago and based on a continuous medium model. The treatment considers the coupling of…
The study of phonon coupling in doped semiconductors via electrical transport measurements is challenging due to unwanted temperature-induced effects such as dopant ionisation and parallel conduction. Here, we study phonon scattering in 2D…
An experimental study of optical phonon modes, both normal and interface (IF) phonons, in bilayer strain-coupled InAs/GaAs_(1-x)Sb_x quantum dot heterostructures has been presented by means of low-temperature polarized Raman scattering. The…
In this work, robustness of controlled density of optical states in doubly driven artificial atoms is studied under phonon dissipation. By using both perturbative and polaron approaches, we investigate the influence of carrier-phonon…
We present a new method for predicting effective thermal conductivity ($\kappa_{\textrm{eff}}$) in materials, informed by ${ab\,initio}$ material property simulations. Using the Boltzmann transport equation in a Self-Adjoint Angular Flux…
Coupling to phonon modes is a primary mechanism of excitonic dephasing and energy loss in semiconductors. However, low-energy phonons in colloidal quantum dots and their coupling to excitons are poorly understood, since their experimental…
A theory of electron-phonon mediated superconductivity requires the knowledge of full phonon spectrum to calculate superconducting transition temperature, T$_c$. However, there is no experimental technique which can measure phonon spectrum…
Although extensive experimental and theoretical works have been conducted to understand the ballistic and diffusive phonon transport in nanomaterials recently, direct observation of temperature and thermal nonequilibrium of different phonon…
The efficient simulation of quantum systems is a primary motivating factor for developing controllable quantum machines. For addressing systems with underlying bosonic structure, it is advantageous to utilize a naturally bosonic platform.…
Recent advances in acquisition equipment is providing experiments with growing amounts of precise yet affordable sensors. At the same time an improved computational power, coming from new hardware resources (GPU, FPGA, ACAP), has been made…
We present a workflow that iteratively combines \textit{ab-initio} calculations with a machine-learning (ML) guided search for superconducting compounds with both dynamical stability and instability from imaginary phonon modes, the latter…
Finite-temperature calculations are relevant for rationalizing material properties yet they are computationally expensive because large system sizes or long simulation times are typically required. Circumventing the need for performing many…
Machine-learning interatomic potentials are widely used as computationally efficient surrogates for density functional theory in atomistic simulations, enabling large-scale, long-time modeling of materials systems. We investigate how…
Single-molecule photoluminescence (PL) spectroscopy of semiconductor nanocrystals (NCs) reveals the nature of exciton-phonon interactions in NCs. Understanding the narrow line shapes at low temperatures and the significant broadening as…
We demonstrate the use of deep learning for fast spectral deconstruction of speckle patterns. The artificial neural network can be effectively trained using numerically constructed multispectral datasets taken from a measured spectral…
We investigate the influence of vibrational screening on the excitonic and optical properties of solids based on first-principles electronic-structure calculations. We solve the Bethe-Salpeter equation - the state-of-the-art description of…
Accurately simulating molecular vibronic spectra remains computationally challenging due to the exponential scaling of required calculations. Here, we show that employing the linear coupling model within the gaussian boson sampling…