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Author

Tsveta Miteva

Bio: Tsveta Miteva is an academic researcher from University of Paris. The author has contributed to research in topics: Interatomic Coulombic decay & Ionization. The author has an hindex of 10, co-authored 37 publications receiving 237 citations. Previous affiliations of Tsveta Miteva include Heidelberg University & Sofia University.

Papers
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Journal ArticleDOI
TL;DR: The authors use electron–electron coincidence detection to find the competitive roles of proton transfer and ICD that occur on similar time scales in water clusters and infer an intrinsic ICD lifetime of 12–52 fs for small water clusters.
Abstract: Intermolecular Coulombic decay (ICD) is a ubiquitous relaxation channel of electronically excited states in weakly bound systems, ranging from dimers to liquids. As it is driven by electron correla ...

38 citations

Journal ArticleDOI
TL;DR: It is demonstrated that the manifold of ICD states populated in the resonant Auger process comprises two groups, one consists of lower energy ionization satellites characterized by fast interatomic decay, while the other consists of slow decaying higher energy ionized satellites.
Abstract: A scheme utilizing excitation of core electrons followed by the resonant-Auger – interatomic Coulombic decay (RA-ICD) cascade was recently proposed as a means of controlling the generation site and energies of slow ICD electrons. This control mechanism was verified in a series of experiments in rare gas dimers. In this article, we present fully ab initio computed ICD electron and kinetic energy release spectra produced following 2p3/2 → 4s, 2p1/2 → 4s, and 2p3/2 → 3d core excitations of Ar in Ar2. We demonstrate that the manifold of ICD states populated in the resonant Auger process comprises two groups. One consists of lower energy ionization satellites characterized by fast interatomic decay, while the other consists of slow decaying higher energy ionization satellites. We show that accurate description of nuclear dynamics in the latter ICD states is crucial for obtaining theoretical electron and kinetic energy release spectra in good agreement with the experiment.

27 citations

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TL;DR: A virtual photon description of three-body ICD is developed, allowing us to investigate retardation and geometrical effects which are out of reach for current ab initio techniques and shows that a passive atom can have a significant influence on the rate of the ICD process at fairly large interatomic distances.
Abstract: Interatomic Coulombic decay (ICD) is a mechanism that allows microscopic objects to rapidly exchange energy. When the two objects are distant, the energy transfer between the donor and acceptor species takes place via the exchange of a virtual photon. On the contrary, recent ab initio calculations have revealed that the presence of a third passive species can significantly enhance the ICD rate at short distances due to the effects of electronic wave function overlap and charge transfer states [Phys. Rev. Lett. 119, 083403 (2017)]. Here, we develop a virtual photon description of three-body ICD, allowing us to investigate retardation and geometrical effects which are out of reach for current ab initio techniques. We show that a passive atom can have a significant influence on the rate of the ICD process at fairly large interatomic distances, due to the scattering of virtual photons off the mediator. Moreover, we demonstrate that in the retarded regime ICD can be substantially enhanced or suppressed depending on the position of the ICD-inactive object, even if the latter is far from both donor and acceptor species.

19 citations

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TL;DR: In this article, the authors employed the R-matrix method to compute ab initio these cross sections for a singly charged neon ion embedded in small helium clusters and showed that the ICEC cross sections are several orders of magnitude higher than anticipated and dominate other competing processes.
Abstract: Interatomic Coulombic electron capture (ICEC) is an environment-assisted process in which a free electron can efficiently attach to an ion, atom or molecule by transferring the excess energy to a neighboring species. Absolute cross sections are necessary to evaluate the relative importance of this process. In this work, we employ the R-matrix method to compute ab initio these cross sections for a singly charged neon ion embedded in small helium clusters. Our results show that the ICEC cross sections are several orders of magnitude higher than anticipated and dominate other competing processes. Electron energy loss spectra on an absolute scale are provided for the Ne+@He20 cluster. Such spectra exhibit an unambiguous signature of the ICEC process. The finding is expected to stimulate experimental observations.

18 citations

Journal ArticleDOI
TL;DR: The second-order algebraic construction (ADC(2) approach to the two-particle propagator, devised to compute double ionization energies and associated spectroscopic amplitudes, is reformulated and extended using the concept of intermediate state representations (ISR).
Abstract: The second-order algebraic construction (ADC(2)) approach to the two-particle (pp) propagator, devised to compute double ionization energies and associated spectroscopic amplitudes, is reformulated and extended using the concept of intermediate state representations (ISR). The ISR formulation allows one to go beyond the general limitations inherent to the propagator approach, as here (N−2)-electron wave functions and properties become directly accessible. The (N−2)-electron ISR(2) equations for a general one-particle operator have been derived and implemented in a recent version of the double ionization ADC(2) program. As a first test of the method, the dipole moments of a series of 2h states of LiH, HF, and H2O were computed and compared to the results of a full configuration interaction (FCI) treatment. The dipole moments obtained at the ADC(2)/ISR(2) computational level are in good agreement with the FCI results.

15 citations


Cited by
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TL;DR: An efficient deep learning approach is developed that enables spatially and chemically resolved insights into quantum-mechanical observables of molecular systems, and unifies concepts from many-body Hamiltonians with purpose-designed deep tensor neural networks, which leads to size-extensive and uniformly accurate chemical space predictions.
Abstract: Learning from data has led to paradigm shifts in a multitude of disciplines, including web, text and image search, speech recognition, as well as bioinformatics. Can machine learning enable similar breakthroughs in understanding quantum many-body systems? Here we develop an efficient deep learning approach that enables spatially and chemically resolved insights into quantum-mechanical observables of molecular systems. We unify concepts from many-body Hamiltonians with purpose-designed deep tensor neural networks, which leads to size-extensive and uniformly accurate (1 kcal mol−1) predictions in compositional and configurational chemical space for molecules of intermediate size. As an example of chemical relevance, the model reveals a classification of aromatic rings with respect to their stability. Further applications of our model for predicting atomic energies and local chemical potentials in molecules, reliable isomer energies, and molecules with peculiar electronic structure demonstrate the potential of machine learning for revealing insights into complex quantum-chemical systems.

570 citations

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TL;DR: In this paper, a deep multi-task artificial neural network is used to predict multiple electronic ground-and excited-state properties, such as atomization energy, polarizability, frontier orbital eigenvalues, ionization potential, electron affinity, and excitation energies.
Abstract: The combination of modern scientific computing with electronic structure theory can lead to an unprecedented amount of data amenable to intelligent data analysis for the identification of meaningful, novel, and predictive structure-property relationships. Such relationships enable high-throughput screening for relevant properties in an exponentially growing pool of virtual compounds that are synthetically accessible. Here, we present a machine learning (ML) model, trained on a data base of \textit{ab initio} calculation results for thousands of organic molecules, that simultaneously predicts multiple electronic ground- and excited-state properties. The properties include atomization energy, polarizability, frontier orbital eigenvalues, ionization potential, electron affinity, and excitation energies. The ML model is based on a deep multi-task artificial neural network, exploiting underlying correlations between various molecular properties. The input is identical to \emph{ab initio} methods, \emph{i.e.} nuclear charges and Cartesian coordinates of all atoms. For small organic molecules the accuracy of such a "Quantum Machine" is similar, and sometimes superior, to modern quantum-chemical methods---at negligible computational cost.

456 citations

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TL;DR: An overview is given of the major developments made in electronic-structure theory for the purpose of simulating advanced X-ray spectroscopies, covering methods based on density-functional theory as well as wave function theory.
Abstract: During the past decade, the research field of computational X-ray spectroscopy has witnessed an advancement triggered by the development of advanced synchrotron light sources and X-ray free electron lasers that in turn has enabled new sophisticated experiments with needs for supporting theoretical investigations. Following a discussion about fundamental conceptual aspects of the physical nature of core excitations and the concomitant requirements on theoretical methods, an overview is given of the major developments made in electronic-structure theory for the purpose of simulating advanced X-ray spectroscopies, covering methods based on density-functional theory as well as wave function theory. The capabilities of these theoretical approaches are illustrated by an overview of simulations of selected linear and nonlinear X-ray spectroscopies, including X-ray absorption spectroscopy (XAS), X-ray natural circular dichroism (XNCD), X-ray emission spectroscopy (XES), resonant inelastic X-ray scattering (RIXS),...

219 citations

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TL;DR: In this article, the authors present a classification scheme for the family of high-dimensional neural network potentials (HDNNPs) and discuss the applicability and remaining limitations of these potentials along with an outlook at possible future developments.
Abstract: Since their introduction about 25 years ago, machine learning (ML) potentials have become an important tool in the field of atomistic simulations After the initial decade, in which neural networks were successfully used to construct potentials for rather small molecular systems, the development of high-dimensional neural network potentials (HDNNPs) in 2007 opened the way for the application of ML potentials in simulations of large systems containing thousands of atoms To date, many other types of ML potentials have been proposed continuously increasing the range of problems that can be studied In this review, the methodology of the family of HDNNPs including new recent developments will be discussed using a classification scheme into four generations of potentials, which is also applicable to many other types of ML potentials The first generation is formed by early neural network potentials designed for low-dimensional systems High-dimensional neural network potentials established the second generation and are based on three key steps: first, the expression of the total energy as a sum of environment-dependent atomic energy contributions; second, the description of the atomic environments by atom-centered symmetry functions as descriptors fulfilling the requirements of rotational, translational, and permutation invariance; and third, the iterative construction of the reference electronic structure data sets by active learning In third-generation HDNNPs, in addition, long-range interactions are included employing environment-dependent partial charges expressed by atomic neural networks In fourth-generation HDNNPs, which are just emerging, in addition, nonlocal phenomena such as long-range charge transfer can be included The applicability and remaining limitations of HDNNPs are discussed along with an outlook at possible future developments

186 citations

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TL;DR: A review of the post-Forster outlook on Resonance Energy Transfer (RET) can be found in this article, where the authors present a survey of the latest research on RET, which includes transfer between nanomaterials.
Abstract: Resonance energy transfer (RET), the transport of electronic energy from one atom or molecule to another, has significant importance to a number of diverse areas of science. Since the pioneering experiments on RET by Cario and Franck in 1922, the theoretical understanding of the process has been continually refined. This review presents a historical account of the post-Forster outlook on RET, based on quantum electrodynamics, up to the present-day viewpoint. It is through this quantum framework that the short-range, R–6 distance dependence of Forster theory was unified with the long range, radiative transfer governed by the inverse-square law. Crucial to the theoretical knowledge of RET is the electric dipole-electric dipole coupling tensor; we outline its mathematical derivation with a view to explaining some key physical concepts of RET. The higher order interactions that involve magnetic dipoles and electric quadrupoles are also discussed. To conclude, a survey is provided on the latest research, which includes transfer between nanomaterials, enhancement due to surface plasmons, possibilities outside the usual ultraviolet or visible range and RET within a cavity.

167 citations