Related papers: Machine Learning Small Polaron Dynamics
Ferroelectric materials with switchable spontaneous polarization underpin non-volatile memories, transistors, sensors, and emerging neuromorphic chips. Their performance and stability are governed by polarization dynamics and domain…
Polaron defects are ubiquitous in materials and play an important role in many processes involving carrier mobility, charge transfer and surface reactivity. Determining the spatial distribution of small polarons is essential to understand…
The formation of polarons is a pervasive phenomenon in transition metal oxide compounds, with a strong impact on the physical properties and functionalities of the hosting materials. In its original formulation the polaron problem considers…
In polarizable materials, electronic charge carriers interact with the surrounding ions, leading to quasiparticle behaviour. The resulting polarons play a central role in many materials properties including electrical transport, optical…
Polaron-mediated charge transport in {\alpha}-Fe2O3 plays a central role in its performance as a gas-sensing material, yet the atomistic interaction between surface adsorbates and polarons remains insufficiently understood. Here, density…
There is a growing understanding that transport properties of complex oxides and individual molecules are dominated by polaron physics. In superconducting oxides the long-range Froehlich and short-range Jahn-Teller electron-phonon…
The multifaceted physics of oxides is shaped by their composition and the presence of defects, which are often accompanied by the formation of polarons. The simultaneous presence of polarons and defects, and their complex interactions, pose…
Machine learning of scalar molecular properties such as potential energy has enabled widespread applications. However, there are relatively few machine learning models targeting directional properties, including permanent and transition…
Simulating the dynamics of ions near polarizable nanoparticles (NPs) using coarse-grained models is extremely challenging due to the need to solve the Poisson equation at every simulation timestep. Recently, a molecular dynamics (MD) method…
In recent years, machine learning interatomic potentials (MLIPs) have attracted significant attention as a method that enables large-scale, long-time atomistic simulations while maintaining accuracy comparable to electronic structure…
Small polarons remain a significant bottleneck in the realization of efficient devices using transition metal oxides. Routes to engineer small polaron coupling to electronic states and lattice modes to control carrier localization remain…
Small polaron behavior in a two dimensional honeycomb net is studied by applying the strong coupling perturbative method to the Holstein molecular crystal model. We find that small optical polarons can be mobile also if the electrons are…
Predicting the electrical properties of organic molecular crystals (OMCs) is challenging due to their complex crystal structures and electron-phonon (e-ph) interactions. Charge transport in OMCs is conventionally categorized into two…
The properties of mobile impurities in quantum magnets are fundamental for our understanding of strongly correlated materials and may play a key role in the physics of high-temperature superconductivity. Hereby, the motion of hole-like…
Polarons are among the most fundamental quasiparticles emerging in interacting many-body systems, forming already at the level of a single mobile dopant. In the context of the two-dimensional Fermi-Hubbard model, such polarons are predicted…
Lead halide perovskite semiconductors are soft, polar, materials. The strong driving force for polaron formation (the dielectric electron-phonon coupling) is balanced by the light band effective-masses, leading to a strongly-interacting…
We present a minimal model of charge transport in hybrid perovskites, which provides an intuitive explanation for the recently proposed formation of ferroelectric large polarons. We demonstrate that short-ranged charge--rotor interactions…
The 2D lattice gas model with competing short and long range interactions is appliedused for calculation of the incoherent charge transport in the classical strongly-correlated charge segregated polaronic state. We show, by means of…
Port-Hamiltonian neural networks (pHNNs) are emerging as a powerful modeling tool that integrates physical laws with deep learning techniques. While most research has focused on modeling the entire dynamics of interconnected systems, the…
We explore polaronic quantum transport in three-dimensional models of disordered organic crystals with strong coupling between electronic and vibrational degrees of freedom. By studying the polaron dynamics in a static disorder environment,…