Related papers: Machine Learning Small Polaron Dynamics
We develop a computational method to learn a molecular Hamiltonian matrix from matrix-valued time series of the electron density. As we demonstrate for three small molecules, the resulting Hamiltonians can be used for electron density…
The interaction of condensed phase systems with external electric fields is crucial in myriad processes in nature and technology ranging from the field-directed motion of cells (galvanotaxis), to energy storage and conversion systems…
Reversible bipolar nano-switches that can be set and read electronically in a solid-state two-terminal device are very promising for applications. We have performed molecular-dynamics simulations that mimic systems with oxygen vacancies…
Polaritonic lattices offer a unique testbed for studying nonlinear driven-dissipative physics. They show qualitative changes of a steady state as a function of system parameters, which resemble non-equilibrium phase transitions. Unlike…
Dynamical simulations of polaron transport in conjugated polymers in the presence of an external time-dependent electric field have been performed within a combined extended Hubbard model (EHM) and Su-Schrieffer-Heeger (SSH) model. Nearly…
Due to solvation, excess charge carriers on 1d semiconductor nanostructures immersed in polar solvents undergo self-localization into polaronic states. Using a simplified theoretical model for small-diameter structures, we study…
We present a model which describes proton scattering data from ISR to Tevatron energies, and which can be applied to collimation n high energy accelerators, such as the LHC and FCC. Collimators remove beam halo particles, so that they do…
Exact diagonalization calculations show a continuous transition from delocalized to small polaron behavior as a function of intersite electron-lattice coupling. A transition, found previously at Hartree-Fock level [Yonemitsu et al., Phys.…
Highly energetic electron-hole pairs (hot carriers) formed from plasmon decay in metallic nanostructures promise sustainable pathways for energy-harvesting devices. However, efficient collection before thermalization remains an obstacle for…
Magneto-polaronic effects are considered in electron transport through a single-level vibrating quantum dot subjected to a transverse (to the current flow) magnetic field. It is shown that the effects are most pronounced in the regime of…
Machine learning has emerged as a potent computational tool for expediting research and development in solid oxide fuel cell electrodes. The effective application of machine learning for performance prediction requires transforming…
Boron phosphide (BP) is a (super)hard semiconductor constituted of light elements, which is promising for high demand applications at extreme conditions. The behavior of BP at high temperatures and pressures is of special interest but is…
When a mobile hole is doped into an antiferromagnet, its movement will distort the surrounding magnetic order and yield a magnetic polaron. The resulting complex interplay of spin and charge degrees of freedom gives rise to very rich…
We study the electronic transport through short DNA chains with various sequences of base pairs between voltage-biased leads. The strong coupling of the charge carriers to local vibrations of the base pairs leads to the formation of…
We study the impact of an external magnetic field on the long-range electron transport in quasi-one-dimensional materials, such as polypeptides, (semi-) conducting polymers and macromolecules, taking into account the electron-lattice…
We consider a polaron model where molecular \emph{rotations} are important. Here, the usual hopping between neighboring sites is affected directly by the electron-phonon interaction via a {\em twist-dependent} hopping amplitude. This model…
Elucidating transport mechanisms and predicting transport coefficients is crucial for advancing material innovation, design, and application. Yet, state-of-the-art calculations are restricted to exact simulations of small lattices with…
We extend the Feynman variational method applied to the parabolic-band Fr\"ohlich (continuum) large polaron~\cite{Feynman1955} to a Holstein (lattice) small polaron, with a parabolic-band. This new theory shows a discrete localisation as a…
Pseudo-Hamiltonian neural networks (PHNN) were recently introduced for learning dynamical systems that can be modelled by ordinary differential equations. In this paper, we extend the method to partial differential equations. The resulting…
Optical neural networks are emerging as a powerful and versatile tool for processing optical signals directly in the optical domain with superior speed, integrability, and functionality. Their application to optical polarization enables…