Publications associées à Excitonic effects in two-dimensional TiSe2 from hybrid density functional theory

Dataset to accompany publication "Quantum-mechanical effects in photoluminescence from thin crystalline gold films"

Giulia Tagliabue, Alan Richard Bowman, Fateme Kiani Shahvandi, Theodoros Tsoulos

This dataset accompanies the publication "Quantum-mechanical effects in photoluminescence from thin crystalline gold films" published in Light: Science & Applications (https://doi.org/10.1038/s41377-024-01408-2). The data can be used to reproduce plots 1-4 ...

Zenodo2024

Exciton migration in two-dimensional materials

Excitons play an essential role in the optical response of two-dimensional materials. These are bound states showing up in the band gaps of many-body systems and are conceived as quasiparticles formed by an electron and a hole. By performing real-time simu ...

Nature Portfolio2024

Quantum-mechanical effects in photoluminescence from thin crystalline gold films

Giulia Tagliabue, Alan Richard Bowman, Fateme Kiani Shahvandi, Theodoros Tsoulos

Luminescence constitutes a unique source of insight into hot carrier processes in metals, including those in plasmonic nanostructures used for sensing and energy applications. However, being weak in nature, metal luminescence remains poorly understood, its ...

2024

Electronic structure and lattice dynamics of 1T-VSe2:Origin of the three-dimensional charge density wave

Lorenzo Monacelli, Ji Dai

To characterize in detail the charge density wave (CDW) transition of 1T-VSe2, its electronic structure and lattice dynamics are comprehensively studied by means of x-ray diffraction, muon spectroscopy, angle resolved photoemission (ARPES), diffuse and ine ...

Amer Physical Soc2024

Electronic transport in graphene with out-of-plane disorder

Oleg Yazyev, Yifei Guan

Real-world samples of graphene often exhibit various types of out-of-plane disorder-ripples, wrinkles and folds-introduced at the stage of growth and transfer processes. These complex out-of-plane defects resulting from the interplay between self-adhesion ...

Nature Portfolio2024

High-Quality Data Enabling Universality of Band Gap Descriptor and Discovery of Photovoltaic Perovskites

Alfredo Pasquarello, Haiyuan Wang, Wei Chen

Extensive machine-learning-assisted research has been dedicated to predicting band gaps for perovskites, driven by their immense potential in photovoltaics. Yet, the effectiveness is often hampered by the lack of high-quality band gap data sets, particular ...

Amer Chemical Soc2024

Mixed ionic-electronic conduction in Ruddlesden-Popper and Dion-Jacobson layered hybrid perovskites with aromatic organic spacers

Michael Graetzel, Jovana Milic, Algirdas Ducinskas, Joachim Maier, Davide Moia, Mina Jung

The understanding of mixed ionic-electronic conductivity in hybrid perovskites has enabled major advances in the development of optoelectronic devices based on this class of materials. While recent investigations revealed the potential of using dimensional ...

Royal Society of Chemistry2024

Realization of Organocerium-Based Fullerene Molecular Materials Showing Mott Insulator-Type Behavior

Pragati Pandey, Xiaoyu Wang

Electron-rich organocerium complexes (C5Me4H)(3)Ce and [(C5Me5)(2)Ce(ortho-oxa)], with redox potentials E-1/2 = -0.82 V and E-1/2 = -0.86 V versus Fc/Fc(+), respectively, were reacted with fullerene (C-60) in different stoichiometries to obtain molecular m ...

Amer Chemical Soc2024

Multi-machine benchmark of the self-consistent 1D scrape-off layer model DIV1D from stagnation point to target with SOLPS-ITER

Holger Reimerdes

This paper extends a 1D dynamic physics-based model of the scrape-off layer (SOL) plasma, DIV1D, to include the core SOL and possibly a second target. The extended model is benchmarked on 1D mapped SOLPS-ITER simulations to find input settings for DIV1D th ...

Iop Publishing Ltd2024

Encoding quantum-chemical knowledge into machine-learning models of complex molecular properties

Ksenia Briling

Statistical (machine-learning, ML) models are more and more often used in computational chemistry as a substitute to more expensive ab initio and parametrizable methods. While the ML algorithms are capable of learning physical laws implicitly from data, ad ...

EPFL2024