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Register a new userFrom absorption spectra to charge transfer in PEDOT nanoaggregates with machine learning
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Fast and inexpensive characterization of materials properties is a key element to discover novel functional materials. In this work, we suggest an approach employing three classes of Bayesian machine learning (ML) models to correlate electronic absorption spectra of nanoaggregates with the strength of intermolecular electronic couplings in organic conducting and semiconducting materials. As a specific model system, we consider PEDOT:PSS, a cornerstone material for organic electronic applications, and so analyze the couplings between charged dimers of closely packed PEDOT oligomers that are at the heart of the materials unrivaled conductivity. We demonstrate that ML algorithms can identify correlations between the coupling strengths and the electronic absorption spectra. We also show that ML models can be trained to be transferable across a broad range of spectral resolutions, and that the electronic couplings can be predicted from the simulated spectra with an 88 % accuracy when ML models are used as classifiers. Although the ML models employed in this study were trained on data generated by a multi-scale computational workflow, they were able to leverage leverage experimental data.
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Charge transfer multiplet (CTM) theory is a computationally undemanding and highly mature method for simulating the soft X-ray spectra of first-row transition metal complexes. However, CTM theory has seldom been applied to the simulation of excited state spectra. In this article, we extend the CTM4XAS software package to simulate M2,3- and L2,3-edge spectra of excited states of first-row transition metals and to interpret CTM eigenfunctions in terms of Russell-Saunders term symbols. We use these new programs to reinterpret the recently reported excited state M2,3-edge difference spectra of photogenerated ferrocenium cations and propose alternative assignments for the electronic state of the photogenerated ferrocenium cations supported by CTM theory simulations. We also use these new programs to model the L2,3-edge spectra of FeII compounds during nuclear relaxation following photoinduced spin crossover, and propose spectroscopic signatures for their vibrationally hot states
Time-resolved spectroscopy provides the main tool for analyzing the dynamics of excitonic energy transfer in light-harvesting complexes. To infer time-scales and effective coupling parameters from experimental data requires to develop numerical exact theoretical models. The finite duration of the laser-molecule interactions and the reorganization process during the exciton migration affect the location and strength of spectroscopic signals. We show that the non-perturbative hierarchical equations of motion (HEOM) method captures these processes in a model exciton system, including the charge transfer state.
We present ab initio absorption spectra of six three-dimensional semiconductors and insulators calculated using Gaussian-based periodic equation-of-motion coupled-cluster theory with single and double excitations (EOM-CCSD). The spectra are calculated efficiently by solving a system of linear equations at each frequency, giving access to an energy range of tens of eV without explicit enumeration of excited states. We assess the impact of Brillouin zone sampling, for which it is hard to achieve convergence due to the cost of EOM-CCSD. Although our most converged spectra exhibit lineshapes that are in good agreement with experiment, they are uniformly shifted to higher energies by about 1 eV. We tentatively attribute this discrepancy to a combination of vibrational effects and the remaining electron correlation, i.e., triple excitations and above.
The excited state dynamics of chromophores in complex environments determine a range of vital biological and energy capture processes. Time-resolved, multidimensional optical spectroscopies provide a key tool to investigate these processes. Although theory has the potential to decode these spectra in terms of the electronic and atomistic dynamics, the need for large numbers of excited state electronic structure calculations severely limits first principles predictions of multidimensional optical spectra for chromophores in the condensed phase. Here, we leverage the locality of chromophore excitations to develop machine learning models to predict the excited state energy gap of chromophores in complex environments for efficiently constructing linear and multidimensional optical spectra. By analyzing the performance of these models, which span a hierarchy of physical approximations, across a range of chromophore-environment interaction strengths, we provide strategies for the construction of ML models that greatly accelerate the calculation of multidimensional optical spectra from first principles.
Energy transfer from photoexcited zero-dimensional systems to metallic systems plays a prominent role in modern day materials science. A situation of particular interest concerns the interaction between a photoexcited dipole and an atomically thin metal. The recent discovery of graphene layers permits investigation of this phenomenon. Here we report a study of fluorescence from individual CdSe/ZnS nanocrystals in contact with single- and few-layer graphene sheets. The rate of energy transfer is determined from the strong quenching of the nanocrystal fluorescence. For single-layer graphene, we find a rate of ~ 4ns-1, in agreement with a model based on the dipole approximation and a tight-binding description of graphene. This rate increases significantly with the number of graphene layers, before approaching the bulk limit. Our study quantifies energy transfer to and fluorescence quenching by graphene, critical properties for novel applications in photovoltaic devices and as a molecular ruler.
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