Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2504.19615

Probing plexciton dynamics with higher-order spectroscopy

Simon Büttner·Luca Nils Philipp·Julian Lüttig·Maximilian Rödel·Matthias Hensen·Jens Pflaum·Roland Mitric·Tobias Brixner
View original source ↗Read source document ↗Export metadata ↓

Abstract

Coupling molecular transition dipole moments to surface-plasmon polaritons (SPPs) results in the formation of new optical quasiparticles, i.e., plexcitons. Mixing the specific properties of matter excitations and light modes has proven to be an efficient strategy to alter a variety of molecular processes ranging from chemical reactions to exciton transport. Here, we investigate energy transfer in a plexcitonic system of zinc phthalocyanine (ZnPc) molecules aggregated in the crystalline α-phase and an SPP on a planar gold surface. By tuning the angle of incidence, we vary the degree of mixing between excitonic and SPP character of the excited state. We apply our recently developed higher-order pump-probe spectroscopy to separate the system's fifth-order signal describing the dynamics of two-particle interactions. The time it takes for two quasiparticles to meet and annihilate is a measure of their movement and thus the transport of excitation energy in the system. We find that the transport extracted from the fifth-order signal is surprisingly unaffected by the mixing ratio of exciton and SPP contributions of the plexciton. Using a rate equation model, we explain this behavior by fast transition from the plexcitonic states to many localized excitonic dark states that do not have an SPP contribution. Our results give an indication of how hybrid exciton-plasmon systems should be designed to exploit the delocalization of the involved plasmon modes for improved transport.

Explore related subjects

physics.chem-phcond-mat.mes-hallphysics.optics

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Simon Büttner, Luca Nils Philipp, Julian Lüttig, Maximilian Rödel, Matthias Hensen, Jens Pflaum, Roland Mitric, Tobias Brixner. 2025-07-28. Probing plexciton dynamics with higher-order spectroscopy. https://doi.org/10.1063/5.0278118

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Global approximations to the error function of real argument for vectorized computation

The error function of real argument can be approximated to a given uniform relative accuracy by a single closed-form expression for the whole variable range either in terms of addition, multiplication, division, and square root operations only, or also using the exponential function. The coefficients have been tabulated for up to 128-bit precision. Tests of a computer code implementation using the standard single- and double-precision floating-point arithmetic show good performance and vectorizability. Approximations to the complementary error function have also been found, including those where only additions, multiplications, and one division are needed to reach a uniform absolute accuracy.

physics.chem-ph↗
arXiv2026

From Heuristics to Machine Learning: The Performance Ceiling for Single-Ion Magnets and Its Electronic Origin

Machine learning (ML) is expected to speed up the discovery of single-ion magnets (SIMs), but does the structural information available before synthesis allow such predictions? For 1215 lanthanide complexes from the SIMDAVIS 1.2.1 database we compared three increasing levels of structural description: tabular features of the coordination site, continuous symmetry measures of the coordination polyhedron, and the complete 3D arrangement of atoms. All three converge to an accuracy near 76%, only slightly above the 71% of the single rule "predict SIM for Dy3+". To explain the failures, we combined multireference ab initio calculations with an inspection of the structures behind the high-confidence errors. The SIMs missed by the geometric models are field-induced relaxers whose ground Kramers doublets are prone to tunnelling, a property invisible to geometric descriptors. Many false positives contain several lanthanide centers or radicals, so their relaxation is collective and outside the single-ion picture. The electronic-structure and connectivity information needed to identify SIMs is therefore not accessible to geometric methods alone. Geometric models remain useful: restricting the screening to compounds with high prediction confidence raises the accuracy to 88% while retaining 48% of the dataset. Building on the analysis of the failures, we propose a strategy that combines simple filters for nuclearity and for radicals with ligand-field descriptors from ab initio calculations.

physics.chem-ph↗
arXiv2026

Composition-Dependent Self-Diffusion Coefficients in Liquid Mixtures from Hybrid Machine Learning

Self-diffusion coefficients are key descriptors of molecular mobility, yet experimental data remain scarce, highlighting the need for reliable prediction methods. In previous work, we introduced the hybrid Enhanced Stokes-Einstein (ESE) model, which advanced the state of the art in the physically consistent prediction of self-diffusion coefficients of solutes at infinite dilution in pure solvents by integrating the Stokes-Einstein equation with machine learning (ML). Here, we extend this approach to concentration-dependent self-diffusion coefficients and multicomponent solvents with HADES. This hybrid architecture leverages a deep-set neural network to connect pure-component and mixture prediction within a single framework. HADES predicts self-diffusion coefficients in liquid mixtures with any number of components at any composition and temperature. The only required inputs are SMILES-encoded molecular structures of the components and the pure-component viscosities, making the method broadly applicable. Trained and evaluated on a comprehensive dataset of 2526 data points for 600 systems, HADES significantly outperforms benchmark prediction methods. The trained model and its source code are fully disclosed, and the application is available via an interactive website https://ml-prop.mv.rptu.de/.

physics.chem-ph↗