相关论文: Machine Learning Exciton Dynamics

The concept of an exciton as a quasiparticle that represents collective excited states was originally adapted from solid-state physics and has been successfully applied to molecular aggregates by relying on the well-established limits of…

Time-dependent density functional theory (TDDFT) is presently enjoying enormous popularity in quantum chemistry, as a useful tool for extracting electronic excited state energies. This article discusses how TDDFT is much broader in scope,…

We study fractional quantum Hall states in double layer systems that can be interpreted as exciton condensates of composite fermions. An electron in one layer is dressed by two fluxes from the same layer and two fluxes from the other layer…

We demonstrate a machine learning based approach which can learn the time-dependent electronic excitation dynamics of small molecules subjected to ion irradiation. Ensembles of recurrent neural networks are trained on data generated by…

Time-dependent density functional theory (TDDFT) is presently enjoying enormous popularity in quantum chemistry, as a useful tool for extracting electronic excited state energies. This article explains what TDDFT is, and how it differs from…

This thesis designs a prediction system based on matrix factorization to predict the classification accuracy of a specific model on a particular dataset. In this thesis, we conduct comprehensive empirical research on more than fifty…

Machine Learning (ML)-based force fields are attracting ever-increasing interest due to their capacity to span spatiotemporal scales of classical interatomic potentials at quantum-level accuracy. They can be trained based on high-fidelity…

The description of excited state dynamics in multichromophoric systems constitutes both a theoretical and experimental challenge in modern physical chemistry. An experimental protocol which can systematically characterize both coherent and…

The first step to gain optical control over the ultrafast processes initiated by light in solids is a correct identification of the physical mechanisms at play. Among them, exciton formation has been identified as a crucial phenomenon which…

We present a theoretical description of femtosecond laser induced dynamics of the hydrogen molecule and of singly ionised sodium dimers, based on a real-space, real-time, implementation of time-dependent density functional theory (TDDFT).…

This paper demonstrates the ability of neural networks to reliably learn the nonlinear flame response of a laminar premixed flame, while carrying out only one unsteady CFD simulation. The system is excited with a broadband, low-pass…

The coupled exciton-vibrational dynamics of a three-site model of the FMO complex is investigated using the Multi-layer Multi-configuration Time-dependent Hartree (ML-MCTDH) approach. Emphasis is put on the effect of the spectral density on…

We develop a quantum scattering model to describe the exciton transport through the Fenna-Matthews-Olson(FMO) complex. It is found that the exciton transport involved the optimal quantum coherence is more efficient than that involved…

Electromagnetic multipole expansion theory underpins nanoscale light-matter interactions, particularly within subwavelength meta-atoms, paving the way for diverse and captivating optical phenomena. While conventionally brute force…

We compare the recently introduced multi-state mapping approach to surface hopping (MASH) with the F\"orster and Redfield theories of excitation energy transfer. Whereas F\"orster theory relies on weak coupling between chromophores, and…

Time-Dependent Density Functional Theory (TDDFT) has been currently established as a computationally cheaper, yet effective, alternative to the Many-Body Perturbation Theory (MBPT) for calculating the optical properties of solids. Within…

We present a computationally efficient framework for predicting the excited-state properties of thermally activated delayed fluorescence (TADF) emitters, integrating extended tight-binding (\xtb), simplified Tamm-Dancoff approximation…

The conversion of $\mathrm{CO_2}$ to value-added compounds is an important part of the effort to store and reuse atmospheric $\mathrm{CO_2}$ emissions. Here we focus on $\mathrm{CO_2}$ hydrogenation over so-called inverse catalysts:…

Primitive photosynthetic cells appear over three billion years prior to any other more complex life-forms, thus it is reasonable to assume that Nature has designed a photosynthetic mechanism using minimal resources but honed to perfection…

We present an investigation into diffusion models for molecular generation, with the aim of better understanding how their predictions compare to the results of physics-based calculations. The investigation into these models is driven by…