Search arXivSearch

arXiv · 2607.00898

Real-time simulation of charge migration within the time-dependent Kohn-Sham DFT

Abstract

Attosecond technologies provide unique opportunities to study electron dynamics and electron correlation on their intrinsic timescales. From a theoretical perspective, this places strong constraints as an accurate treatment of electron correlation is required. Recently, it was demonstrated that time-dependent density-functional theory (TDDFT) is capable of correctly predicting correlation-driven charge migration arising from hole mixing following ionization of the highest occupied molecular orbital (HOMO). Given the ability of TDDFT to treat large-scale systems, this approach offers promising perspectives for investigating electron-correlation-driven mechanisms in complex molecules. In this work, we assessed the constraints and limitations associated with using TDDFT to study this mechanism. We found that the charge-migration dynamics are already correctly reproduced using local-density approximation for the exchange-correlation functional, provided the states involved in the coherent superposition are well described within the TDDFT. However, for dynamics triggered by the ionization of orbitals below the HOMO, artificial ultrafast dynamics may appear on top of the charge-migration dynamics. These artifacts indicate that careful analysis of the simulated dynamics is required in order to reliably predict phenomena that could be observed experimentally.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Rajarshi Sinha-Roy, Clément Guiot du Doignon, Evan Munaro-Langloÿs, Franck Rabilloud, Victor Despré. 2026-07-01. Real-time simulation of charge migration within the time-dependent Kohn-Sham DFT. https://arxiv.org/abs/2607.00898

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

KEEP EXPLORING

Related papers

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

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

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