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Related publications (29)

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Tuning Conductivity and Spin Dynamics in Few-Layer Graphene via In Situ Potassium Exposure

László Forró, Bálint Náfrádi, Péter Szirmai, Ferenc Simon

Chemical modification such as intercalation or doping of novel materials is of great importance for exploratory material science and applications in various fields of physics and chemistry. Herein, the systematic intercalation of chemically exfoliated few- ...

2020

Band Gap of Atomically Precise Graphene Nanoribbons as a Function of Ribbon Length and Termination

Christoph Leopold Talirz

We study the band gap of finite NA=7 armchair graphene nanoribbons (7-AGNRs) on Au(111) through scanning tunneling microscopy/spectroscopy combined with density functional theory calculations. The band gap of 7-AGNRs with lengths of 8 nm and more is conver ...

WILEY-V C H VERLAG GMBH2019

Exploring The Borderlands Of The Gathering Problem

Rachid Guerraoui, El Mahdi El Mhamdi, Alexandre David Olivier Maurer

Problems of pattern formation have been extensively studied in distributed computing. One of this problems is the gathering problem: agents must gather at a same position in a distributed manner. When gathering is not possible, a close problem is the conve ...

2019

Electrically tunable quantum emitters in an ultrathin graphene-hexagonal boron nitride van der Waals heterostructure

Klaus Kern, Marko Burghard, Shai Mangel, Alessio Scavuzzo

We explore the photoluminescence (PL) properties of hexagonal boron nitride (h-BN) quantum emitters embedded within atomically thin graphene/h-BN heterostructures fabricated by mechanical transfer. Stable light emission could be observed from h-BN emitters ...

AMER INST PHYSICS2019

Transferable Machine-Learning Model of the Electron Density

Michele Ceriotti, Anne-Clémence Corminboeuf, Alberto Fabrizio, David Mark Wilkins, Benjamin André René Meyer, Andrea Grisafi

The electronic charge density plays a central role in determining the behavior of matter at the atomic scale, but its computational evaluation requires demanding electronic-structure calculations. We introduce an atom-centered, symmetry-adapted framework t ...

2019

Machine Learning at the Atomic Scale

Michele Ceriotti, Félix Benedito Clément Musil

Statistical learning algorithms are finding more and more applications in science and technology. Atomic-scale modeling is no exception, with machine learning becoming commonplace as a tool to predict energy, forces and properties of molecules and condense ...

SWISS CHEMICAL SOC2019

Universal noise in continuous transport measurements of interacting fermions

Jean-Philippe Brantut

We propose and analyze continuous measurements of atom number and atomic currents using dispersive probing in an optical cavity. For an atom-number measurement in a closed system, we relate both the detection noise and the heating rate due to measurement b ...

2018

Feature optimization for atomistic machine learning yields a data-driven construction of the periodic table of the elements

Michele Ceriotti, Félix Benedito Clément Musil, Michael John Willatt

Machine-learning of atomic-scale properties amounts to extracting correlations between structure, composition and the quantity that one wants to predict. Representing the input structure in a way that best reflects such correlations makes it possible to im ...

2018

Symmetry breakdown of electron emission in extreme ultraviolet photoionization of argon

Philip Johannes Walter Moll, Eugenio Ferrari, David Ferdinand Scholz

Short wavelength free-electron lasers (FELs), providing pulses of ultrahigh photon intensity, have revolutionized spectroscopy on ionic targets. Their exceptional photon flux enables multiple photon absorptions within a single femtosecond pulse, which in t ...

Nature Publishing Group2018